Low Earth orbit (LEO) mega-constellations enable global, low-latency connectivity but challenge the security function chain (SFC) orchestration due to fast-changing visibility, resource volatility, and heterogeneous quality of service (QoS) requirements. To tackle this issue, we present in this paper SATStab, a stability-aware orchestration framework that co-designs: (i) stability-regularized objectives penalizing configuration churn across time windows; (ii) temporal decoupling with visibility-aware repair and warm starts; and (iii) hierarchical decoupling that separates fast association decisions from slower SFC placement and resource allocation. We formulate SATStab as a mixed-integer non-linear programming (MINLP) problem and evaluate it with realistic LEO dynamics. We solve it through a two-stage approach, where in the first stage, we optimize satellite-user associations while in the second stage, we optimize SFC placements. Through extensive simulations, we show that, relative to a monolithic MINLP, SATStab reduces user-satellite handovers by 54.3%, satellite-ground station (GS) re-associations by 59.3%, and SFC migrations by 32.7%, while cutting solution time by 56.8% without degrading end-to-end (E2E) delay. These results prove the efficiency of SATStab in terms of resource orchestration and resilience.

This paper presents a distributed beamforming framework for a constellation of airborne platform stations (APSs) in a massive Multiple-Input and Multiple-Output (MIMO) non-terrestrial network (NTN) that targets the downlink sum-rate maximization under imperfect local channel state information (CSI). We propose a novel entropy-based multi-agent deep reinforcement learning (DRL) approach where each non-terrestrial base station (NTBS) independently computes its beamforming vector using a Fourier Neural Operator (FNO) to capture long-range dependencies in the frequency domain. To ensure scalability and robustness, the proposed framework integrates transfer learning based on a conjugate prior mechanism and a low-rank decomposition (LRD) technique, thus enabling efficient support for large-scale user deployments and aerial layers. Our simulation results demonstrate the superiority of the proposed method over baseline schemes including WMMSE, ZF, MRT, CNN-based DRL, and the deep deterministic policy gradient (DDPG) method in terms of average sum rate, robustness to CSI imperfection, user mobility, and scalability across varying network sizes and user densities. Furthermore, we show that the proposed method achieves significant computational efficiency compared to CNN-based and WMMSE methods, while reducing communication overhead in comparison with shared-critic DRL approaches.

Low Earth orbit (LEO) satellite networks present critical challenges for security function chain (SFC) orchestration and associations due to rapid topology changes, resource volatility, and heterogeneous service requirements that render conventional SFC optimization approaches ineffective. To tackle this issue, we introduce here STARS, an optimization framework that fundamentally transforms the sequential time-window optimization for SFC orchestration and satellite association through three techniques: (1) Stability-aware regularization that penalizes configuration changes across time windows, thus reducing handovers by 54% and security function migrations by 33%; (2) Temporal decoupling that leverages solutions from prior time windows as warm-start seeds and dynamic repairing using real-time visibility constraints; and (3) Hierarchical decoupling that separates satellite association and SFC placement into computationally efficient stages, thus reducing time complexity. Through rigorous formulation as a mixed-integer non-linear programming (MINLP) and simulation-based evaluation, STARS achieves a 57% reduction in optimization solution time, a 7% reduction in the load of deployed security function instances, and efficient CPU utilization (9.81% increase) compared to benchmark schemes. STARS delivers these substantial benefits without any degradation in end-to-end delay. Note that the reported performance values are based on our specific system parameter choices and simulation setup and may not be universally representative. The co-design of stability mechanisms and decoupling strategies establishes STARS as a new paradigm for resilient satellite network optimization, balancing optimality, continuity, and computational tractability under high LEO satellite dynamicity.

The low Earth orbit (LEO) satellite megaconstellation can provide ubiquitous coverage and high-performance connectivity, supporting multi-slice applications with various key performance indicator (KPI) requirements. However, due to the dynamic nature of LEO satellites, limited resources, and the diverse demands of different slices, managing user association (UA) and resource allocation becomes an increasingly challenging task in areas with overlapping satellite coverage. This paper proposes a joint optimization model for UA and resource allocation in satellite networks (SLSNs). Based on mixed-integer non-linear programming (MILP), our model minimizes the total propagation delay and optimizes the demand satisfaction ratio (DSR) using a Max-Min approach to ensure each slice meets its unique throughput requirements. In addition, a visibility-aware component is incorporated to prioritize longer satellite visibility, reduce handovers, and improve network stability. Due to the computational complexity of the MILP model, we propose a heuristic-based balanced association with delay-aware bandwidth distribution (B-DAD) approach. B-DAD operates in two phases: the initial UA phase selects satellites based on a combined metric of delay, load, and visibility duration, while the residual bandwidth distribution phase reallocates unused bandwidth among associated users proportionally. Extensive simulations demonstrate that our approaches significantly improve DSR, propagation delays, transmission delays, and network stability compared to the widely adopted benchmark maximum sum of data rate (Max-SR) and Greedy methods under varying elevation angles. Our findings highlight the effectiveness of the MILP model in achieving optimal solutions and the efficiency of B-DAD as a scalable alternative for large-scale scenarios.

Cell-free massive multiple-input multiple-output (CF-mMIMO) has emerged as a new paradigm shift for future wireless communication networks to cope with the limitations of traditional cellular architectures. The deployment of CF-mMIMO networks comes with challenges, such as enabling reliable and low-latency fronthaul connections, particularly for isolated geographical areas where the fiber optics are costly to deploy or wireless fronthaul links are inaccessible. To address this, high altitude platform station (HAPS) emerges as a promising solution for fronthauling and ensuring cooperation tasks as a central processing unit (CPU) in the CF-mMIMO networks. The HAPS located in the stratosphere provides a wide coverage area, low latency, and cost-efficient fronthauling. Therefore, cell-free networks can achieve seamless connectivity between the distributed access points (APs) at a higher effective data rate by leveraging the HAPS. This paper investigates the sum rate maximization problem in the HAPS-assisted CF-mMIMO systems. Performance evaluations demonstrate that the proposed system design increases users’ data rates by leveraging both the high-capacity fronthauling supported by HAPS and the superior spatial multiplexing gain provided by CF-mMIMO. Furthermore, the study highlights the potential of HAPS-assisted architectures in providing services for users in underserved areas.

This paper proposes an innovative approach by leveraging uncrewed aerial vehicles (UAVs) as base stations (BSs) and a high-altitude platform station (HAPS) as the central processing unit (CPU) in an integrated sensing and communication (ISAC) system for 6G networks. We explore the challenges, applications, and advantages of ISAC systems in next-generation networks, highlighting the significance of optimizing position and power control. Our approach integrates HAPS and UAVs to enhance wireless coverage, particularly in remote areas. UAVs function as dual-purpose access points (APs), using their maneuverability and line-of-sight (LoS) aerial-to-ground (A2G) links to transmit combined communication and sensing signals. The scheme operates in two time slots: in the first slot, UAVs transmit dedicated signals to communication users (CUs) and potential targets. UAVs detect targets in specific ground locations and, after signal transmission, receive reflected signals from targets. In the second slot, UAVs relay these signals to HAPS, which performs beamforming to align signals for each CU from various UAVs. UAVs decode information from HAPS and adjust transmissions to maximize the efficiency of the beam pattern toward the desired targets. We formulate a multi-objective optimization problem with the goal of maximizing both the minimum signal-to-interference-plus-noise ratio (SINR) for CUs and the echo signal power from the targets. This is achieved by finding the optimal power allocation for CUs in each UAV, subject to constraints on the maximum total power in each UAV and the transmitted beam pattern gain. Simulation results demonstrate the effectiveness of this approach in enhancing network performance, resource allocation, fairness, and system optimization. By utilizing HAPS as the CPU, computational tasks are offloaded from UAVs, which conserves energy and further improves overall network performance.

High altitude platform stations (HAPS) offer a promising solution for achieving ubiquitous connectivity in next-generation wireless networks (xG). Integrating HAPS with terrestrial networks, creating HAPS-empowered vertical heterogeneous networks (vHetNets), significantly improves coverage and capacity and supports emerging novel use cases. In HAPS-empowered vHetNets, HAPS and terrestrial network tiers can share the same spectrum, forming harmonized spectrum vHetNets that enhance spectral efficiency (SE). However, harmonized spectrum vHetNets face major challenges, including severe co-channel interference and scalability in large-scale deployments. To address the first challenge, we adopt a cell-free multiple-input multiple-output (MIMO) network architecture in which users are simultaneously served by multiple base stations using beamforming. However, beamforming weight design leads to a nonconvex, high-dimensional optimization problem, highlighting the scalability challenge. To address this second challenge, we develop a two-level distributed proportional fairness beamforming weight design (PFBWD) algorithm. This algorithm combines the augmented Lagrangian method (ALM) with a three-block ADMM framework. Simulation results demonstrate the performance improvements achieved by integrating HAPS with standalone terrestrial networks, as well as the reduced complexity and signaling overhead of the distributed algorithm compared to centralized algorithms.

Faster-than-Nyquist (FTN) signaling is a non-orthogonal transmission technique offering a promising solution for future generations of communications. This paper studies the capacity of FTN signaling in multiple-input multiple-output (MIMO) channels for high acceleration factors. In our previous study [1], we found the capacity for MIMO FTN channels if the acceleration factor is larger than a certain threshold, which depends on the bandwidth of the pulse shape used. In this paper we extend the capacity analysis to acceleration factors smaller than this mentioned threshold. In addition to capacity, we conduct instantaneous-to-average power ratio (IAPR) analysis and simulation for MIMO FTN for varying acceleration factors for both Gaussian and QPSK symbol sets. Our analysis reveals important insights about transmission power and received signal-to-noise ratio (SNR) variation in FTN. As the acceleration factor approaches 0, if the transmission power is fixed, the received SNR diminishes, or if the received SNR is fixed, IAPR at the transmitter explodes.

The surge in wireless connectivity demand, coupled with the finite nature of spectrum resources, compels the development of efficient spectrum management approaches. Spectrum sharing presents a promising avenue, although it demands precise characterization of spectrum demand for informed policy-making. This paper introduces HR-GAT, a hierarchical resolution graph attention network model, designed to predict spectrum demand using geospatial data. HR-GAT adeptly handles complex spatial demand patterns and resolves issues of spatial autocorrelation that usually challenge standard machine learning models, often resulting in poor generalization. Tested across five major Canadian cities, HR-GAT improves predictive accuracy of spectrum demand by 21% over eight baseline models, underscoring its superior performance and reliability.

The modern Internet supports diverse applications with heterogeneous quality of service (QoS) requirements. Low Earth orbit (LEO) satellite constellations offer a promising solution to meet these needs, enhancing coverage in rural areas and complementing terrestrial networks in urban regions. Ensuring QoS in such networks requires joint optimization of routing, bandwidth allocation, and dynamic queue scheduling, as traffic handling is critical for maintaining service performance. This paper formulates a joint routing and bandwidth allocation problem where QoS requirements are treated as soft constraints, aiming to maximize user experience. An adaptive scheduling approach is introduced to prioritize flow-specific QoS needs. We propose a Monte Carlo tree search (MCTS)-inspired method to solve the NP-hard route and bandwidth allocation problem, with Lyapunov optimization-based scheduling applied during reward evaluation. Using the Starlink Phase 1 Version 2 constellation, we compare end-user experience and fairness between our proposed DSROQ algorithm and a benchmark scheme. Results show that DSROQ improves both performance metrics and demonstrates the advantage of joint routing and bandwidth decisions. Furthermore, we observe that the dominant performance factor shifts from scheduling to routing and bandwidth allocation as traffic sensitivity changes from latency-driven to bandwidth-driven.

Large-scale low-Earth-orbit (LEO) constellations demand routing that simultaneously minimizes energy, guarantees delivery under congestion, and meets latency requirements for time-critical flows. We present a segment routing over IPv6 (SRv6) flexible algorithm (Flex-Algo) framework that consists of three logical slices: an energy-efficient slice (Algo 130), a high-reliability slice (Algo 129), and a latency-sensitive slice (Algo 128). The framework provides a unified mixed-integer linear program (MILP) that combines satellite CPU power, packet delivery rate (PDR), and end-to-end latency into a single objective, allowing a lightweight software-defined network (SDN) controller to steer traffic from the source node. Emulation of Telesat’s Lightspeed constellation shows that, compared with different routing schemes, the proposed design reduces the average CPU usage by 73%, maintains a PDR above 91% during traffic bursts, and decreases urgent flow delay by 18 ms between Ottawa and Vancouver. The results confirm Flex-Algo’s value as a slice-based traffic engineering (TE) tool for resource-constrained satellite networks.

Integrated Terrestrial-Satellite Backhaul Networks (ITSBN) offer wide coverage and cost effectiveness, especially in remote or underserved areas, where backhaul links are facilitated through a multi-beam mechanism. In this context, a Beam Hopping Plan (BHP) can enhance resource efficiency by dynamically activating beams based on traffic demand, improving coverage flexibility. However, despite its several advantages, ITSBN may encounter congestion challenges during periods of high traffic demand. Content caching may be a solution to alleviate the network load, enabling content delivery at the network edge and reducing backhaul traffic. In this work, we consider ITSBN with caching at local base stations (BSs) and aim to maximize the system throughput. To achieve this, we propose a double-layer iterative algorithm. The outer layer employs our proposed$\varepsilon$-Beam Hopping Plan and Content Selection algorithm ($\varepsilon$-BHPCS), leveraging the matching game approach. This method jointly designs the BHP and selects content carried by each beam, balancing accuracy and execution time through the parameter$\varepsilon \in[0, 1]$. For inner layer, we propose the Alternating Direction Method of Multipliers Based Power Allocation and Caching (ADMM-PAC) scheme, optimizing power across beams and managing caching at BSs in a distributed fashion, using BSs' resources and local information for parallel optimization processes without requiring global information as in a centralized method. Simulations show that$\varepsilon$-BHPCS achieves performance similar to the optimal exhaustive search, while the distributed ADMM-PAC aligns closely with standard centralized interior-point optimization algorithm.

Internet of Things (IoT) devices have become increasingly ubiquitous with applications not only in urban areas but remote areas as well. These devices support industries such as agriculture, forestry, and resource extraction. Due to the device location being in remote areas, satellites are frequently used to collect and deliver IoT device data to customers. As these devices become increasingly advanced and numerous, the amount of data produced has rapidly increased potentially straining the ability for radio frequency (RF) downlink capacity. Free space optical communications with their wide available bandwidths and high data rates are a potential solution, but these communication systems are highly vulnerable to weather-related disruptions. This results in certain communication opportunities being inefficient in terms of the amount of data received versus the power expended. In this paper, we propose a deep reinforcement learning (DRL) method using Deep Q-Networks that takes advantage of weather condition forecasts to improve energy efficiency while delivering the same number of packets as schemes that don't factor weather into routing decisions. We compare this method with simple approaches that utilize simple cloud cover thresholds to improve energy efficiency. In testing the DRL approach provides improved median energy efficiency without a significant reduction in median delivery ratio. Simple cloud cover thresholds were also found to be effective but the thresholds with the highest energy efficiency had reduced median delivery ratio values.

Non-terrestrial base stations (NTBSs), including high-altitude platform stations (HAPSs) and hot-air balloons (HABs), are integral to next-generation wireless networks, offering coverage in remote areas and enhancing capacity in dense regions. In this paper, we propose a distributed beamforming framework for a massive MIMO network with a constellation of aerial platform stations (APSs). Our approach leverages an entropy-based multi-agent deep reinforcement learning (DRL) model, where each APS operates as an independent agent using imperfect channel state information (CSI) in both training and testing phases. Unlike conventional methods, our model does not require CSI sharing among APSs, significantly reducing overhead. Simulations results demonstrate that our method outperforms zero forcing (ZF) and maximum ratio transmission (MRT) techniques, particularly in high-interference scenarios, while remaining robust to CSI imperfections. Additionally, our framework exhibits scalability, maintaining stable performance over an increasing number of users and various cluster configurations. Therefore, the proposed method holds promise for dynamic and interference-rich NTBS networks, advancing scalable and robust wireless solutions.

The integration of low Earth orbit (LEO) satellite communication into 6G networks promises a transformative impact on global connectivity by expanding coverage to remote regions and enhancing service reliability. However, this new infrastructure also introduces significant security challenges due to its expansive attack surface. To address this concern, we propose a dynamic security functions allocation (SFA) model that optimizes the allocation of security functions (SFs) across satellites while considering computational resource limitations, dynamic topology changes, and the visibility constraints of satellite constellations. Our model leverages the flexibility of 6G network slicing (NS) to share non-critical SFs between slices, reducing resource overhead while maintaining essential security demands. To minimize the risk of sharing highly sensitive SFs between slices, our model employs a nonlinear penalty, which prioritizes minimizing risk by aggressively penalizing high-risk SFs sharing. This dynamic risk management framework assesses the probability and impact of security breaches, ensuring that SFs are shared only when the security risk is acceptable, balancing resource efficiency and security. By dynamically adapting to the network’s operational conditions, our approach provides a robust framework for efficient and secure satellite communication in 6G networks. Simulation results demonstrate the model’s flexibility in managing trade-offs across key network performance metrics.

Path loss modeling is a widely used technique for estimating point-to-point losses along a communications link from transmitter (Tx) to receiver (Rx). Accurate path loss predictions can optimize use of the radio frequency spectrum and minimize unwanted interference. Modern path loss modeling often leverages data-driven approaches, using machine learning to train models on drive test measurement datasets. Drive tests primarily represent downlink scenarios, where the Tx is located on a building and the Rx is located on a moving vehicle. Consequently, trained models are frequently reserved for downlink coverage estimation, lacking representation of uplink scenarios. In this paper, we demonstrate that data augmentation can be used to train a path loss model that is generalized to uplink, downlink, and backhaul scenarios, training using only downlink drive test measurements. By adding a small number of synthetic samples representing uplink scenarios to the training set, root mean squared error is reduced by > 8 dB on uplink examples in the test set.

Accurate spectrum demand prediction is crucial for informed spectrum allocation, effective regulatory planning, and fostering sustainable growth in modern wireless communication networks. It supports governmental efforts, particularly those led by the international telecommunication union (ITU), to establish fair spectrum allocation policies, improve auction mechanisms, and meet the requirements of emerging technologies such as advanced 5G, forthcoming 6G, and the internet of things (IoT). This paper presents an effective spatio-temporal prediction framework that leverages crowdsourced user-side key performance indicators (KPIs) and regulatory datasets to model and forecast spectrum demand. The proposed methodology achieves superior prediction accuracy and cross-regional generalizability by incorporating advanced feature engineering, comprehensive correlation analysis, and transfer learning techniques. Unlike traditional ITU models, which are often constrained by arbitrary inputs and unrealistic assumptions, this approach exploits granular, data-driven insights to account for spatial and temporal variations in spectrum utilization. Comparative evaluations against ITU estimates, as the benchmark, underscore our framework’s capability to deliver more realistic and actionable predictions. Experimental results validate the efficacy of our methodology, highlighting its potential as a robust approach for policymakers and regulatory bodies to enhance spectrum management and planning.

Software-defined networking (SDN) has emerged as a promising approach for managing traditional satellite communication. This enhances opportunities for future services, including integrating satellite and terrestrial networks. In this paper, we have developed an SDN-enabled framework for Low Earth Orbit (LEO) satellite networks, incorporating the Open-Flow protocol, all within an OMNeT++ simulation environment. Dynamic controller assignment is one of the most significant challenges for large LEO constellations. Due to the movement of LEO satellites, satellite-controller assignments must be updated frequently to maintain low propagation delays. To address this issue, we present a dynamic satellite-to-controller assignment (DSCA) optimization problem that continuously adjusts these assignments. Our optimal DSCA (Opt-DSCA) approach minimizes propagation delay and optimizes the number of active controllers. Our preliminary results demonstrate that the DSCA approach significantly outperforms the static satellite-to-controller assignment (SSCA) approach. While SSCA may perform better with more controllers, this scheme fails to adapt to satellite movements. Our DSCA approach consistently improves network efficiency by dynamically reassigning satellites based on propagation delays. Further, we found diminishing returns when the number of controllers is increased beyond a certain point, suggesting optimal performance with a limited number of controllers. Opt-DSCA lowers propagation delays and improves network performance by optimizing satellite assignments and reducing active controllers.

Sixth-Generation (6G) networks are designed to meet the hyper-reliable and low-latency communication (HRLLC) requirements of safety-critical applications such as autonomous driving. Integrating non-terrestrial networks (NTN) into the 6G infrastructure brings redundancy to the network, ensuring continuity of communications even under extreme conditions. In particular, high-altitude platform stations (HAPS) stand out for their wide coverage and low latency advantages, supporting communication reliability and enhancing information freshness, especially in rural areas and regions with infrastructure constraints. In this paper, we present reinforcement learning-based approaches using deep deterministic policy gradient (DDPG) to dynamically optimize the age-of-information (AoI) in HAPS-enabled vehicle-to-everything (V2X) networks. The proposed method improves information freshness and overall network reliability by enabling independent learning without centralized coordination. The findings reveal the potential of HAPS-supported solutions, combined with DDPG-based learning, for efficient AoI-aware resource allocation in platoon-based autonomous vehicle systems.

This paper presents a high-altitude platform station (HAPS)-enabled integrated sensing and communication (ISAC) system designed for sixth-generation (6G) networks. Positioned in the stratosphere, HAPS serves as a super-macro base station, leveraging advanced beamforming techniques to enable communication and sensing simultaneously. This research addresses the need for equitable service distribution in 6G networks by focusing on fairness within the HAPS-ISAC system. It tackles a non-convex optimization problem that balances sensing beampattern gain and signal-to-interference-plus-noise ratio (SINR) requirements among communication users (CUs) using a max-min fairness approach while adhering to power constraints. The proposed HAPS-ISAC framework ensures efficient resource allocation, reliable coverage, and improved sensing accuracy. Simulation results validate the potential of HAPS-ISAC as a pivotal enabler for 6G networks and integrated communication-sensing systems.

This paper investigates the integration of active reconfigurable intelligent surfaces (RIS) relay with high-altitude platform stations (HAPS) to enhance non-terrestrial network (NTN) performance in next-generation wireless systems. While prior studies focused on passive RIS architectures, the severe path loss and double fading in long-distance HAPS links make active RIS a more suitable alternative due to its inherent signal amplification capabilities. We formulate a sum-rate maximization problem to jointly optimize power allocation and RIS element assignment for ground user equipments (UEs) supported by a HAPS-based active RIS-assisted communication system. To reduce power consumption and hardware complexity, several sub-connected active RIS architectures are also explored. Simulation results reveal that active RIS configurations significantly outperform passive RIS in terms of quality of service (QoS). Moreover, although fully-connected architectures achieve the highest throughput, sub-connected schemes demonstrate superior energy efficiency under practical power constraints. These findings highlight the potential of active RIS-enabled HAPS systems to meet the growing demands of beyond-cellular coverage and green networking.

In this paper, a new waveform called discrete Fourier transform spread orthogonal frequency division multiplexing with chirp modulation (DFT-s-OFDM-CM) is proposed for the next generation of wireless communications. The information bits are conveyed by not only Q-ary constellation symbols but also the starting frequency of chirp signal. It could maintain the benefits provided by the chirped discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-s-OFDM), e.g., low peak-to-average power ratio (PAPR), full frequency diversity exploitation, etc. Simulation results confirm that the proposed DFT-s-OFDM-CM could achieve higher spectral efficiency while keeping the similar bit error rate (BER) to that of chirped DFT-s-OFDM. In addition, when maintaining the same spectral efficiency, the proposed DFT-s-OFDM-CM with the splitting of information bits into two streams enables the use of lower-order constellation modulation and offers greater resilience to noise, resulting in a lower BER than the chirped DFT-s-OFDM.

This study introduces and addresses the critical challenge of traffic load estimation in cell switching within vertical heterogeneous networks (vHetNets). The effectiveness of cell switching is significantly limited by the lack of accurate traffic load data for small base stations (SBSs) in sleep mode, making many load-dependent energy-saving approaches impractical, as they assume perfect knowledge of traffic loads—an assumption that is unrealistic when SBSs are inactive. In other words, when SBSs are in sleep mode, their traffic loads cannot be directly known and can only be estimated, inevitably with corresponding errors. Rather than proposing a new switching algorithm, we focus on eliminating this foundational barrier by exploring effective prediction techniques. A novel vHetNet model is considered, integrating a high-altitude platform station (HAPS) as a super macro base station (SMBS). We investigate both spatial and temporal load estimation approaches, including three spatial interpolation schemes—random neighboring selection, distance-based selection, and multi-level clustering (MLC)—alongside a temporal deep learning method based on long short-term memory (LSTM) networks. Using a real-world dataset for empirical validation, our results show that both spatial and temporal methods significantly improve estimation accuracy, with the MLC and LSTM approaches demonstrating particularly strong performance.

This paper proposes a convolutional neural network (CNN)-based detector for faster-than-Nyquist (FTN) signaling that employs structured fixed kernel layers with domain-informed masking to mitigate intersymbol interference (ISI). Unlike standard CNNs with sliding kernels, the proposed method utilizes fixed-position kernels to directly capture ISI effects at varying distances from the central symbol. A hierarchical filter allocation strategy is also introduced, assigning more filters to earlier layers for strong ISI patterns and fewer to later layers for weaker ones. This design improves detection accuracy while reducing redundant operations. Simulation results show that the detector achieves near-optimal bit error rate (BER) performance for τ ≥ 0.7, closely matching the BCJR algorithm, and offers computational gains of up to 46% and 84% over M-BCJR for BPSK and QPSK, respectively. Comparative analysis with other methods further highlights the efficiency and effectiveness of the proposed approach. To the best of our knowledge, this is the first application of a fixed-kernel CNN architecture tailored for FTN detection in the literature.

Faster-than-Nyquist signaling serves as a promising solution for improving spectral efficiency in future generations of communications. However, its nature of fast acceleration brings highly overlapped pulses that lead to worse peak-to-average power ratio (PAPR) performance. In this paper, we investigate the PAPR behavior of MIMO FTN using Gaussian symbols under optimal power allocation for two power constraints: fixed transmit power and fixed received signal-to-noise-ratio (SNR). Our findings reveal that PAPR is mainly determined by the acceleration factor and the power constraint, but power allocation optimization does not change the PAPR behavior for Gaussian signaling.

In order to bolster future wireless networks, there has been a great deal of interest in non-terrestrial networks, especially aerial platforms including high-altitude platform stations (HAPS) and uncrewed aerial vehicles (UAVs). These platforms can integrate advanced technologies such as reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA). In this regard, this paper proposes a multi-layer network architecture consisting of HAPS and UAV, where the former acts as a HAPS super macro base station (HAPS-SMBS), while the latter serves as a relay node for the ground Internet of Things (IoT) devices. The UAV is equipped with active transmissive RIS, which is a novel technology with promising benefits. We also utilize multiple-input single-output (MISO) technology, i.e., multiple antennas at the HAPS-SMBS and a single antenna at the IoT devices. Additionally, we consider NOMA as the multiple access technology as well as the existence of hardware impairments as a practical limitation. We compare the proposed system model with various scenarios, all involving the HAPS-SMBS and RIS-equipped UAV relay combination, but with different types of RIS, antenna configurations, and access technologies. Sum rate and energy efficiency are used as performance metrics, and the findings demonstrate that, in comparison to all benchmarks, the proposed system yields significant performance gains. Moreover, hardware impairment limits the system performance at high transmit power levels.

To overcome the limited endurance of traditional unmanned aerial vehicles (UAVs), we propose a network of robotic aerial base stations (RABSs) that can energy-efficiently anchor into tall urban landforms, such as lampposts. This approach enables the creation of a hyper-flexible wireless multi-hop network, designed to support green, densified, and dynamic network requirements, thereby ensuring reliable long-term coverage for the whole observed region. The proposed network infrastructure can concurrently address the backhaul link capacity bottleneck and support access link traffic demand in the millimeter-wave (mmWave) frequency band. Specifically, the RABSs grasping locations, resource blocks (RBs) assignment, and route flow control are simultaneously optimized to maximize the served traffic demands. The group of RABSs capitalizes on the fact that traffic distribution varies considerably across both time and space within a given geographical area. Hence, they are able to relocate to suitable locations, i.e., ‘follow’ the traffic demand as it unfolds to increase the overall network efficiency. To tackle the curse of dimensionality of the proposed mixed-integer problem, we propose a greedy algorithm to obtain a competitive solution with low computational complexity. A wide set of numerical investigations reveals that RABSs could improve the served traffic demand. For instance, compared to networks with randomly deployed fixed small cells, the proposed mode serves at most 65% more traffic demand.

The pursuit of higher data rates and efficient spectrum utilization in modern communication technologies necessitates novel solutions. In order to provide insights into improving spectral efficiency and reducing latency, this study investigates the maximum channel coding rate (MCCR) of finite block length (FBL) multiple-input multiple-output faster-than-Nyquist (FTN) channels. By optimizing power allocation, we derive the system's MCCR expression. Simulation results are compared with the existing literature to reveal the benefits of FTN in FBL transmission.

This paper presents a novel convolutional neural network (CNN)-based detector for faster-than-Nyquist (FTN) signaling, introducing structured fixed kernel layers with domain-informed masking to effectively mitigate intersymbol interference (ISI). Unlike standard CNN architectures that rely on moving kernels, the proposed approach employs fixed convolutional kernels at predefined positions to explicitly learn ISI patterns at varying distances from the central symbol. To enhance feature extraction, a hierarchical filter allocation strategy is employed, assigning more filters to earlier layers for stronger ISI components and fewer to later layers for weaker components. This structured design improves feature representation, eliminates redundant computations, and enhances detection accuracy while maintaining computational efficiency. Simulation results demonstrate that the proposed detector achieves near-optimal bit error rate (BER) performance, comparable to the BCJR algorithm for the compression factor τ ≥ 0.7, while offering up to 46% and 84% computational cost reduction over M-BCJR for BPSK and QPSK, respectively. Additional evaluations confirm the method’s adaptability to high-order modulations (up to 64-QAM), resilience in quasi-static multipath Rayleigh fading channels, and effectiveness under LDPC-coded FTN transmission, highlighting its robustness and practicality.

A high-altitude platform station (HAPS) located in the stratosphere can provide connectivity over a large area. However, a HAPS can create only a limited number of uncorrelated beams. Therefore, we cannot dedicate a beam to each user, and we need to perform user scheduling to be able to serve a large number of users with a HAPS. To this end, in this paper, we group users into clusters using the K-means algorithm and allocate the users in each cluster to orthogonal frequency resource blocks. The frequencies are reused across the clusters. Since HAPS has limited power resources, we also propose a novel codebook-based hybrid beamforming scheme. The results of our simulations conclusively show that our proposed beamforming scheme outperforms the traditional steering vector-based beamforming scheme in high-rise urban areas. Furthermore, we propose a deep Q-network (DQN)-based power allocation method that considerably outperforms the baseline equal power allocation scheme for large coverage areas. Finally, we compare the interference management efficiency of our proposed orthogonal frequency-division multiplexing (OFDM)-based hybrid beamforming-enabled hemispherical array antenna and baseline rectangular and cylindrical array antennas.

Full-array (FA) hybrid beamforming, integrating analog and digital components, is employed in millimeter wave (mmWave) systems to achieve directional signal transmission with enhanced gains. The main hardware challenge in this hybrid configuration resides in the analog segment, where each antenna at the transmitters and receivers is linked to an entire network of phase shifters (PSs). To address this, researchers have investigated sub-array (SA) hybrid designs that use fewer PSs to reduce hardware complexity; however, the number of PSs scales linearly with the number of antenna subarrays. This paper introduces innovative adaptive phase shifters (APSs) designed for hybrid beamforming that feature low hardware complexity and operate independently of the number of antenna arrays and radio frequency (RF) chains. The proposed APSs hybrid scheme is designed to achieve performance comparable to the FA iterative hybrid design while minimizing the number of required PSs. Specifically, the number of PSs can vary from two per RF chain to the entire network of PSs, allowing for a more efficient and high-performance design of hybrid systems. Furthermore, the full network of PSs per RF chain, corresponding to the total number of antenna elements, is reduced using modified K-means algorithms. Simulation results demonstrate that the spectral efficiency performance of APSs hybrid designs using only two PSs surpasses that of conventional FA hybrid design and SA hybrid design and exhibits similar performance to FA iterative hybrid designs with a smaller number of PSs. The proposed APSs hybrid design represents promising technology for 6G systems and beyond, owing to its innovative design with low hardware complexity and high-gain spectral efficiency.

Federated learning is a promising approach to training deep learning models over distributed devices without sharing their local datasets. Low Earth orbit (LEO) satellites have the potential to use the massive amount of collected Earth imageries and sensor data to train artificial intelligence (AI) models and provide global services such as disaster detection. However, the structure of LEO networks is different from that of the terrestrial networks, which makes the traditional (star-based or hierarchical-based FL) inefficient. This paper proposes a new distributed FL approach that is customized to the LEO structure. The approach is based on parallelizing the FL operations and decentralizing the aggregations over several satellites, aiming at reducing the convergence time at a given energy constraint. Simulation results show that the proposed algorithm converges significantly faster than traditional FL-LEO approaches proposed in the literature under the same energy consumption.

Spectrum scarcity necessitates innovative, spectral-efficient strategies to meet the ever-growing demand for high data rates. Faster-than-Nyquist (FTN) signaling emerges as a compelling spectral-efficient transmission method that pushes transmit data symbols beyond the Nyquist limit, offering en-hanced spectral efficiency (SE). While FTN signaling maintains SE with the same energy and bandwidth as the Nyquist signaling, it introduces increased complexity, particularly at higher modulation levels. This complexity predominantly arises from the detection process, which seeks to mitigate the intentional intersymbol interference generated by FTN signaling. Another challenge involves the generation of reliable log-likelihood ratios (LLRs) vital for soft channel decoders. In this study, we introduce a lookup table (LUT) aided soft output semidefinite relaxation (soSDR) based sub-optimal FTN detector, which can be extended to higher modulation levels. This detector possesses polyno-mial computational complexity, given the negligible complexity associated with soft value generation. Our study assesses the performance of this soft output detector against that of the optimal FTN detector, Bahl, Cocke, Jelinek and Raviv (BCJR) algorithm as the benchmark. The likelihood values produced by our LUT aided semidefinite relaxation (SDR) based FTN signaling detector show promising viability in coded scenario.

Waveform generation is essential for studying signal propagation and channel characteristics, particularly for objects that are conceptualized but still need to be operational. We introduce a comprehensive guide on creating synthetic signals using channel and delay coefficients derived from the Quasi-Deterministic Radio Channel Generator (QuaDRiGa), which is recognized as a 3GPP-3D and 3GPP 38.901 reference implementation. The effectiveness of the proposed synthetic waveform generation method is validated through accurate estimation of code delay and Doppler shift. This validation is achieved using both the parallel code phase search technique and the conventional tracking method applied to satellites. As the method of integrating channel and delay coefficients to create synthetic waveforms is the same for satellite, HAPS, and gNB PRS, validating this method on synthetic satellite signals could potentially be extended to HAPS and gNB PRS as well. This study could significantly contribute to the field of heterogeneous navigation systems.

For decades, satellites have facilitated remote internet of things (IoT) services. However, the recent proliferation of increasingly capable sensors and a surge in the number deployed, has led to a substantial growth in the volume of data that needs to be transmitted via satellites. In response to this growing demand, free space optical communication systems have been proposed, as they allow for the use of large bandwidths of unlicensed spectrum, enabling high data rates. However, optical communications are highly vulnerable to weather-induced disruptions, thereby limiting their high potential. This paper proposes the use of high altitude platform station (HAPS) systems in conjunction with delay-tolerant networking techniques to increase the amount of data that can be transmitted to the ground from satellites when compared to the use of traditional ground station network architectures. The architectural proposal is evaluated in terms of delivery ratio and buffer occupancy, and the subsequent discussion analyzes the advantages, challenges and potential areas for future research.

In the diverse landscape of 6G networks, where wireless connectivity demands surge and spectrum resources remain limited, flexible spectrum access becomes paramount. The success of crafting such schemes hinges on our ability to accurately characterize spectrum demand patterns across space and time. This paper presents a data-driven methodology for estimating spectrum demand variations over space and identifying key drivers of these variations in the mobile broadband landscape. By leveraging geospatial analytics and machine learning, the methodology is applied to a case study in Canada to estimate spectrum demand dynamics in urban regions. Our proposed model captures 70% of the variability in spectrum demand when trained on one urban area and tested on another. These insights empower regulators to navigate the complexities of 6G networks and devise effective policies to meet future network demands.

With the emergence of many new 5G and 6G use cases, the demand for spectrum continues to grow. In response, spectrum regulators worldwide are actively exploring innovative approaches to manage spectrum more efficiently. Spectrum sharing emerges as a particularly promising approach, as it allows for more intensive use of spectrum by enabling other services and users to access idle bands. Nevertheless, identifying areas where spectrum is either under- or over-supplied poses a significant challenge for regulators, given that demand insights are typically only observable to mobile operators.This paper proposes DeepSpectrum, a novel Deep Learning (DL) model that employs Multi-Task Learning, to estimate the local demand for mobile spectrum. The proposed model is trained on publicly available geospatial datasets and is used to estimate a novel demand proxy derived from crowdsourced data. The model features a combination of Convolutional Neural Networks and custom Residual Networks to extract relevant patterns from the data itself, thereby eliminating the need for traditional manual feature engineering. Overall, DeepSpectrum achieves a performance improvement of over 20% compared to alternative ML regression algorithms, demonstrating the advantage of DL for more accurate spectrum demand modeling.

In this paper, we present a novel hemispherical antenna array (HAA) designed for High-Altitude Platform Stations (HAPS). Traditional rectangular antenna arrays for HAPS suffer from a significant limitation - their antenna elements are perpetually oriented downward, resulting in low gain for distant users. Meanwhile, cylindrical antenna arrays were introduced to mitigate this drawback, but they, in turn, exhibit a distinct problem: their antenna elements continually face the horizon, leading to suboptimal gain for users located beneath the HAPS. To address these challenges, we introduce the HAA. In the HAA configuration, antenna elements are strategically distributed across the surface of a hemisphere, ensuring that each user receives direct alignment with specific antenna elements, thereby maximizing the gain for all users. We derive the achievable data rates for users within this proposed scheme, employing an analog beamforming technique that leverages the steering vectors of the selected antenna elements for each user. We also formulate an op-timization problem focused on maximizing the minimum Signal-to-Interference-plus-Noise Ratio (SINR) for users. Additionally, we introduce an antenna selection algorithm based on the gains of the antenna elements. To further enhance system performance, we employ the Bisection method to determine the optimal power allocation for each user. Our simulation results substantiate the superior rate performance of the proposed HAA when compared to the conventional rectangular and cylindrical baseline arrays. The proposed approach demonstrates to reach sum data rates of up to 14 Gigabit/s. Furthermore, in contrast to the baseline schemes, the proposed scheme achieves more consistent spectral efficiencies across the entire coverage area.

By exploiting the degree of freedom on the altitude, unmanned aerial vehicle (UAV) communication can provide ubiquitous communication for future wireless networks. In the case of concurrent transmission of multiple UAVs, the directional beamforming formed by multiple antennas is an effective way to reduce co-channel interference. However, factors, such as airflow disturbance or estimation error for UAV communications, can cause the occurrence of beam misalignment. In this article, we investigate the system performance of a multitier UAV communication network with the consideration of unstable beam alignment. In particular, we propose a tractable random model to capture the impacts of beam misalignment in the 3-D space. Based on this, by utilizing stochastic geometry, an analytical framework for obtaining the outage probability in the downlink of a multitier UAV communication network for the closest distance association scheme and the maximum average power association scheme is established. The accuracy of the analysis is verified by Monte Carlo simulations. The results indicate that in the presence of random beam misalignment, the optimal number of UAV antennas needs to be adjusted to be relatively larger when the density of UAVs increases or the altitude of UAVs becomes higher.

The low-Earth orbiting (LEO) satellite constellations in the 90s (such as Iridium and Globalstar), although representing a major technical breakthrough, were not able to achieve the initial goal of complementing the second-generation mobile terrestrial networks due to the rapid worldwide adoption of GSM and other standards. However, the decline of conventional linear television and the persisting need to mitigate the digital divide still affecting billions of people recently generated a renewed interest by private investors for Low-Earth Orbiting (LEO) satellite mega-constellations. The mega-constellations under deployment can provide lower delays versus geostationary (GEO) satellites, broadband access anywhere, anytime leveraging the low-cost series production of small satellites, and more affordable launch solutions. At the end of the last decade, key industrial players realized the potential complementary role satellites can play to extend the 5G terrestrial network coverage over low-density populated areas, oceans, or similar. This has led to great technological developments and a sharp reduction in LEO satellite volume, weight, and, ultimately, manufacturing and launching costs. Also, it has triggered the inclusion of a non terrestrial network (NTN) component in the latest 5G 3GPP standard releases. Yet, there are many technological challenges remaining to ensure that both the quality and cost of the NTN services are comparable to the terrestrial counterpart. This is mainly due to the constraints in satellite payload power, mass, and antenna size. This comes in addition to the stringent power flux density limitations on the ground, in particular in the below 6 GHz satellite bands. The required order of magnitude increase of the effective delivered throughput and service cost reduction can only be achieved by a mix of system architectures and innovative technologies for both the space and ground segments. Space communication systems and technologies will also play a key role in human return to the Moon planned for mid-2020, to prepare for human exploration of Mars in the more distant future and further cosmic exploration. As missions voyage further from Earth, it is important to consider how we can continue to reliably communicate with them and how they will accurately navigate through space when they are so far from home.

Selection of hyperparameters in deep neural networks is a challenging problem due to the wide search space and emergence of various layers with specific hyperparameters. There exists an absence of consideration for the neural architecture selection of convolutional neural networks (CNNs) for spectrum sensing. Here, we develop a method using reinforcement learning and Q-learning to systematically search and evaluate various architectures for generated datasets including different signals and channels in the spectrum sensing problem. We show by extensive simulations that CNN-based detectors proposed by our developed method outperform several detectors in the literature. For the most complex dataset, the proposed approach provides 9% enhancement in accuracy at the cost of higher computational complexity. Furthermore, a novel method using multi-armed bandit model for selection of the sensing time is proposed to achieve higher throughput and accuracy while minimizing the consumed energy. The method dynamically adjusts the sensing time under the time-varying condition of the channel without prior information. We demonstrate through a simulated scenario that the proposed method improves the achieved reward by about 20% compared to the conventional policies. Consequently, this study effectively manages the selection of important hyperparameters for CNN-based detectors offering superior performance of cognitive radio network.

In this paper, fresh Basic Safety Messages (BSM) (e.g., vehicle’s position and speed) are used to control the Connected Automated Vehicles (CAVs) to reduce Time to Collision (TTC) error which leads to decrease in Collision Risk (CR). In contrast to exiting works, a novel Safety-aware Age of Information (S-AoI) metric is proposed that in addition to AoI, takes into account the risk assessment of CAVs to design an efficient transmission protocol for BSMs. We also deploy user-centric Cell-free-massive-MIMO (CFmMIMO) to improve the communication coverage, accessibility, and reliability, where each CAV is served by a cluster of nearby Access Points (APs). Unlike previous works, a two time-scale distributed deterministic policy gradients algorithm is adopted which greatly reduces the signal processing complexity, system load as well as signaling overhead while maintaining the performance. Simulation results show that the proposed framework, i.e, user-centric CFmMIMO technology together with S-AoI metric, can reduce average TTC error between 24%-35% across different lane change probabilities compared to the baseline scenario in which we use small cell mMIMO with AoI metric. Such a reduction in TTC error results in significant decrease (as high as 75%) in CR ratio.

In this paper, we present a novel hemispherical antenna array (HAA) designed for high-altitude platform stations (HAPS). A significant limitation of traditional rectangular antenna arrays for HAPS is that their antenna elements are oriented downward, resulting in low gains for distant users. Cylindrical antenna arrays were introduced to mitigate this drawback; however, their antenna elements face the horizon leading to suboptimal gains for users located beneath the HAPS. To address these challenges, in this study, we introduce our HAA. An HAA’s antenna elements are strategically distributed across the surface of a hemisphere to ensure that each user is directly aligned with specific antenna elements. To maximize users’ minimum signal-to-interference-plus-noise ratio (SINR), we formulate an optimization problem. After performing analog beamforming, we introduce an antenna selection algorithm and show that this method achieves optimality when a substantial number of antenna elements are selected for each user. Additionally, we employ the bisection method to determine the optimal power allocation for each user. Our simulation results convincingly demonstrate that the proposed HAA outperforms the conventional arrays, and provides uniform rates across the entire coverage area. With a 20 MHz communication bandwidth, and a 50 dBm total power, the proposed approach reaches sum rates of 14 Gbps.

We propose a federated learning (FL) in stratosphere (FLSTRA) system, where a high altitude platform station (HAPS) facilitates a large number of terrestrial clients to collaboratively learn a global model without sharing the training data. FLSTRA overcomes the challenges faced by FL in terrestrial networks, such as slow convergence and high communication delay due to limited client participation and multi-hop communications. HAPS leverages its altitude and size to allow the participation of more clients with line-of-sight (LOS) links and the placement of a powerful server. However, handling many clients at once introduces computing and transmission delays. Thus, we aim to obtain a delay-accuracy trade-off for FLSTRA. Specifically, we first develop a joint client selection and resource allocation algorithm for uplink and downlink to minimize the FL delay subject to the energy and quality-of-service (QoS) constraints. Second, we propose a communication and computation resource-aware (CCRA-FL) algorithm to achieve the target FL accuracy while deriving an upper bound for its convergence rate. The formulated problem is non-convex; thus, we propose an iterative algorithm to solve it. Simulation results demonstrate the effectiveness of the proposed FLSTRA system, compared to terrestrial benchmarks, in terms of FL delay and accuracy.

The high altitude platform station (HAPS) technology is garnering significant interest as a viable technology for serving as base stations in communication networks. However, HAPS faces the challenge of high spatial correlation among adjacent users’ channel gains which is due to the dominant line-of-sight (LoS) path between HAPS and terrestrial users. Furthermore, there is a spatial correlation among antenna elements of HAPS that depends on the propagation environment and the distance between elements of the antenna array. This paper presents an antenna architecture for HAPS and considers the mentioned issues by characterizing the channel gain and the spatial correlation matrix of the HAPS. We propose a cylindrical antenna for HAPS that utilizes vertical uniform linear array (ULA) sectors. Moreover, to address the issue of high spatial correlation among users, the non-orthogonal multiple access (NOMA) clustering method is proposed. An algorithm is also developed to allocate power among users to maximize both spectral efficiency and energy efficiency while meeting quality of service (QoS) and successive interference cancellation (SIC) conditions. Finally, simulation results indicate that the spatial correlation has a significant impact on spectral efficiency and energy efficiency in multiple antenna HAPS systems.

Hybrid beamforming (HB) with quality-of-service (QoS) provisioning per stream in millimeter waves is indispensable in 5G/6G networks. HB includes baseband and radio frequency (RF) beamforming, and requires error-free channel state information (CSI), which is erroneous in practice. So there is a need for efficient, feasible, robust, and QoS-aware HB. To achieve this, we mitigate CSI uncertainty via baseband beamforming, and we steer the RF beamformer by using the estimates of the channel’s eigenvectors. In doing so, we consider the effective channel’s uncertainty region instead of the uncertainty region of the channel itself, as the former is smaller than the latter, requiring less transmit power to satisfy the QoS constraint. We also detect and eliminate the infeasible data streams. Our iterative scheme (which is based on the cutting-set method) for baseband beamforming satisfies the mean-squared error (MSE) constraint per stream, where a limited number of constraints are considered instead of infinitely many constraints. In our low-complexity scheme, we derive a simple sufficient condition to check the feasibility of each stream, we diagonalize the effective channel at the baseband precoder, and we use minimum MSE combining at the baseband combiner. Extensive simulations validate our formulations and theoretical derivations.

Faster-than-Nyquist (FTN) signaling is a non-orthogonal transmission technique, which brings in intentional inter-symbol interference. This way it can significantly enhance spectral efficiency for practical pulse shapes such as the root raised cosine pulses. This paper proposes an achievable rate region for the multiple antenna (MIMO) asynchronous multiple access channel (aMAC) with FTN signaling. The scheme applies waterfilling in the spatial domain and precoding in time. Water-filling in space provides better power allocation and precoding helps mitigate inter-symbol interference due to asynchronous transmission and FTN. The results show that the gains due to asynchronous transmission and FTN are more emphasized in MIMO aMAC than in single antenna aMAC. Moreover, FTN improves single-user rates, and asynchronous transmission improves the sum-rate, due to better inter-user interference management.

In this paper, we design a new flexible smart software-defined radio access network (Soft-RAN) architecture with traffic awareness for sixth generation (6G) wireless networks. In particular, we consider a hierarchical resource allocation model for the proposed smart soft-RAN model where the software-defined network (SDN) controller is the first and foremost layer of the framework. This unit dynamically monitors the network to select a network operation type on the basis of distributed or centralized resource allocation procedures to intelligently perform decision-making. In this paper, our aim is to make the network more scalable and more flexible in terms of conflicting performance indicators such as achievable data rate, overhead, and complexity indicators. To this end, we introduce a new metric, i.e, throughput-overhead-complexity (TOC), for the proposed machine learning-based algorithm, which supports a trade-off between these performance indicators. In particular, the decision making based on TOC is solved via deep reinforcement learning (DRL) which determines an appropriate resource allocation policy. Furthermore, for the selected algorithm, we employ the soft actor-critic (SAC) method which is more accurate, scalable, and robust than other learning methods. Simulation results demonstrate that the proposed smart network achieves better performance in terms of TOC compared to fixed centralized or distributed resource management schemes that lack dynamism. Moreover, our proposed algorithm outperforms conventional learning methods employed in recent state-of-the-art network designs.

Distributed computing enables Internet of vehicle (IoV) services by collaboratively utilizing the computing resources from the network edge and the vehicles. However, the computing interruption issue caused by frequent edge network handoffs, and a severe shortage of computing resources are two problems in providing IoV services. High altitude platform station (HAPS) computing can be a promising addition to existing distributed computing frameworks due to its wide coverage and strong computational capabilities. In this regard, this paper proposes an adaptive scheme in a new distributed computing framework that involves HAPS computing to deal with the two problems of the IoV. Based on the diverse demands of vehicles, network dynamics, and the time-sensitivity of handoffs, the proposed scheme flexibly divides each task into three parts and assigns them to the vehicle, roadside units (RSUs), and a HAPS to perform synchronous computing. The proposed scheme also constrains the computing of tasks at RSUs such that they are completed before handoffs to avoid the risk of computing interruptions. We formulate a delay minimization problem that considers task-splitting ratio, transmit power, bandwidth allocation, and computing resource allocation. To solve the problem, variable replacement and successive convex approximation-based methods are proposed. The simulation results show that this scheme not only avoids the negative effects caused by handoffs in a flexible manner but also it improves the delay performance and maintains the delay stability.

Unmanned aerial vehicle (UAV)-enabled edge federated learning (FL) has sparked a rise in research interest as a result of the massive and heterogeneous data collected by UAVs, as well as the privacy concerns related to UAV data transmissions to edge servers. However, due to the redundancy of UAV collected data, e.g., imaging data, and non-rigorous FL participant selection, the convergence time of the FL learning process and bias of the FL model may increase. Consequently, we investigate in this paper the problem of selecting UAV participants for edge FL, aiming to improve the FL model’s accuracy, under UAV constraints of energy consumption, communication quality, and local datasets’ heterogeneity. We propose a novel UAV participant selection scheme, called data-efficient energy-aware participant selection strategy (DEEPS), which consists of selecting the best FL participant in each sub-region based on the structural similarity index measure (SSIM) average score of its local dataset and its power consumption profile. Through experiments, we demonstrate that the proposed selection scheme is superior to the benchmark random selection method, in terms of model accuracy, training time, and UAV energy consumption.

As research in wireless communications shifts towards developing the next generation of mobile networks, the demand for spectrum — specifically, the demand for mobile services — continues to grow. Many 6G verticals are envisioned to have much higher data and capacity requirements, which, in turn, will increase the need for more spectrum. While current demand within a mobile network might be known to its respective operator, it is not easily observable by spectrum regulators. Understanding current demand and identifying potential factors that drive demand can greatly assist regulators in ensuring that spectrum is managed and released efficiently to best support emerging technologies and use cases.In this paper, we leverage a large variety of input features derived from publicly available geospatial datasets in conjunction with a gradient boosting tree-based machine learning model to estimate current demand for mobile services at a local level. The proposed model is able to capture over 60% of the variance in the data for two different test scenarios, effectively outperforming three different baseline algorithms. We also employ a gain-based feature importance algorithm to identify potential key drivers of spectrum demand, which contrast the simple intuition that demand is driven only by population or economic activity. Finally, we build a more sparse and interpretable model to help regulators make more informed spectrum planning decisions.

In this paper, we investigate the performance of reconfigurable intelligent surface (RIS)-assisted aerial communications, where a ground base station (GBS) communicates with distant terrestrial and/or aerial users through the assistance of a RIS-equipped unmanned aerial vehicle (RIS-UAV). The GBS uses the non-orthogonal multiple access (NOMA) scheme to transmit its signal, which is directed to the users via the RIS-UAV. First, the end-to-end channel is characterized, by considering the shadowed Rician fading, then the outage probability performance metric is derived for the underlying system model. Through numerical results, we demonstrate the impact of several system parameters on the performance of NOMA users. Specifically, we found that RIS elements need to be carefully allocated among different NOMA users, according to their channel conditions, in order to achieve the needed quality of service.

Dynamic laser inter-satellite links (LISLs) provide the flexibility of connecting a pair of satellites as required (dynamically) while static LISLs need to be active continuously between the energy-constrained satellites. However, due to the LISL establishment time (termed herein as LISL setup delay) being in the order of seconds, realizing dynamic LISLs is currently unfeasible. Towards the realization of dynamic LISLs, we first study the quantification of LISL setup delay; then we calculate the end-to-end latency of a free-space optical satellite network (FSOSN) with the LISL setup delay; subsequently, we analyze the impact of LISL setup delay on the end-to-end latency of the FSOSN. We also provide design guidelines for the laser communication terminal manufacturers in the form of maximum tolerable value of LISL setup delay for which the FSOSN based on Starlink’s Phase I satellite constellation will be meaningful to use for low-latency long-distance inter-continental data communications.

Internet of Things (IoT) networks require massive connections in dense areas. Therefore, a resource-efficient multiple access scheme seems inevitable to enable immense connectivity where multiple devices have to share the same resource block (RB). Nonorthogonal multiple access (NOMA) has been considered as the strongest candidate in recent years. However, in this article, by considering the practical implementation, we first provide a true power allocation (PA) constraint with finite alphabet inputs for conventional downlink NOMA and demonstrate that it cannot support massive connections in practical systems. To this end, we propose the bit-interleaved multiple access (BIMA) scheme in downlink IoT networks. The proposed BIMA scheme implements bitwise multiaccess interleaving and deinterleaving at the transceiver ends and there are no strict PA constraints, unlike conventional NOMA, thus allowing a high number of devices in the same RB. We provide a comprehensive analytical framework for BIMA by investigating all key performance indicators (KPIs) to present both information-theoretic [i.e., ergodic capacity (EC) and outage probability (OP)] and finite alphabet inputs [i.e., bit error rate (BER)] performance metrics with both instantaneous and statistical channel ordering. In addition, we define Jain’s fairness index and proportional fairness index (PFI) in terms of all KPIs. Based on the extensive computer simulations, we reveal that BIMA outperforms conventional NOMA significantly, with a performance gain of up to 20–30 dB in terms of KPIs in some scenarios. In other words, compared to conventional NOMA schemes, the same KPIs are met in BIMA with 20–30 dB less transmit power, which is quite promising for energy-limited use cases. Moreover, this performance gain becomes greater when more IoT devices are supported. BIMA provides a full diversity order for all IoT devices and enables the implementation of an arbitrary number of devices and modulation orders, which is crucial for IoT networks where a huge number of devices should be supported in a single RB in dense areas. In addition to the overall performance gain, BIMA guarantees a fairness system where none of the devices gets a severely degraded performance and the sum-rate is shared in a fair manner among devices. It guarantees QoS satisfaction for all devices. Finally, we provide an intense complexity and latency analysis for BIMA and demonstrate that it provides lower latency compared to conventional NOMA receivers, since it allows parallel computation at the receivers and no iterative operations are required. We show that compared to conventional NOMA receivers, BIMA reduces latency by up to 350% for specific IoT devices and 170% on average.

Due to the flexible deployment of unmanned aerial vehicles (UAVs) and the wide-area coverage of satellites, the space–air–ground (SAG) communication network can provide flexible and pervasive connectivity, especially in remote areas. In this work, we investigate the uplink transmission in a SAG network, where the nonorthogonal multiple access mechanism is adopted at the UAVs to enhance the number of access from ground user equipments (UEs) and a low-earth orbit satellite offers the wireless backhaul for UAVs. In particular, the energy efficiency (EE) of the considered network is maximized by optimizing the user association (UA), power allocation (PA), and UAV 3-D trajectory jointly with the consideration of the movement of the satellite. To tackle the formulated problem, by leveraging the block coordinate descent (BCD) method, we develop a joint UA, PA, and UAV trajectory (namely, JUPT) optimization algorithm, i.e., the original problem is decomposed into three subproblems, and the subproblems are solved iteratively until convergence. Specifically, we propose to include the virtual UEs in the system and develop a low-complexity matching algorithm to effectively solve the UA problem. A successive convex approximation (SCA)-based Dinkelbach algorithm is then adopted to address the PA problem. Later, with the introduction of the auxiliary variables, the UAV 3-D trajectory subproblem is iteratively solved by the SCA method. Our numerical results demonstrate the superiority of the proposed JUPT algorithm, which obtains significantly higher EE compared to the benchmark schemes. Moreover, the rapid convergence of the JUPT algorithm is verified.

Due to the unprecedented advances in satellite fabrication and deployment, innovative communications and networking technologies, ambitious space projects and programs, and the resurgence of interest in satellite networks, there is a need to redefine space networks (SpaceNets) to incorporate all of these evolutions. This paper introduces a vision for future SpaceNets that considers advances in several related domains. First, we present a reference architecture that captures the various network entities and terminals in a holistic manner. Based on this, space, air, and ground use cases are studied. Then, the architectures and technologies that enable the envisaged SpaceNets are investigated. In so doing, we highlight the activities and projects of different standardization bodies, satellite operators, and national organizations towards the envisioned SpaceNets. Finally, the challenges, potential solutions, and open issues from communications and networking perspectives are discussed.

This paper develops a low-complexity suboptimal non-coherent receiver for a multi-level energy-based coded modulation system. Inspired by the turbo processing principle, we incorporate the fundamentals of bit-interleaved coded modulation with iterative decoding (BICM-ID) into the proposed receiver design. The resulting system is called bit-interleaved coded energy-based modulation with iterative decoding (BICEM-ID) and its error performance is analytically studied. Specifically, we derive upper bounds on the average pairwise error probability (PEP) of the non-coherent BICEM-ID system in the feedback-free (FF) and error-free feedback (EFF) scenarios. It is revealed that the definition of the nearest neighbors, which is important in the performance analysis in the FF scenario, is very different from that in the coherent BICM-ID counterpart. The analysis also reveals how the mapping from coded bits to energy levels influences the diversity order and coding gain of the BICEM-ID systems. A design criterion for good mappings is then formulated, and an algorithm is proposed to find a set of best mappings for BICEM-ID. Finally, simulation results corroborate the main analytical findings.

The various requirements in terms of data rates and latency in beyond 5G and 6G networks have motivated the integration of a variety of communications schemes and technologies to meet these requirements in such networks. Among these schemes are Terahertz (THz) communications, cooperative non-orthogonal multiple-access (NOMA)-enabled schemes, and mobile edge computing (MEC). THz communications offer abundant bandwidth for high-data-rate short-distance applications and NOMA-enabled schemes are promising schemes to realize the target spectral efficiencies and low latency requirements in future networks, while MEC would allow distributed processing and data offloading for the emerging applications in these networks. In this paper, an energy-efficient scheme of multi-user NOMA-assisted cooperative THz single-input multiple-output (SIMO) MEC systems is proposed to allow the uplink transmission of offloaded data from the far cell-edge users to the more computing resources in the base station (BS) through the cell-center users. To reinforce the performance of the proposed scheme, two optimization problems are formulated and solved, namely, the first problem minimizes the total users’ energy consumption while the second problem maximizes the total users’ computation energy efficiency (CEE) for the proposed scheme. In both problems, the NOMA user pairing, the BS receive beamforming, the transmission time allocation, and the NOMA transmission power allocation coefficients are optimized, while taking into account the full-offloading requirements of each user as well as the predefined latency constraint of the system. The obtained results reveal new insights into the performance and design of multi-user NOMA-assisted cooperative THz-SIMO MEC systems. Particularly, with relatively high offloading rate demands (several Gbits/user), we show that (i) the proposed scheme can handle such demands while satisfying the predefined latency constraint, and (ii) the full-offloading model can be considered the most effective solution in conserving mobile devices’ resources as compared to the system with the partial-offloading model or the system without offloading.

Virtualized network slicing allows a multitude of logical networks to be created on a common substrate infrastructure to support diverse services. A virtualized network slice is a logical combination of multiple virtual network functions, which run on virtual machines (VMs) as software applications by virtualization techniques. As the performance of network slices hinges on the normal running of VMs, detecting and analyzing anomalies in VMs are critical. Based on the three-tier management framework of virtualized network slicing, we first develop a federated learning (FL) based three-tier distributed VM anomaly detection framework, which enables distributed network slice managers to collaboratively train a global VM anomaly detection model while keeping metrics data locally. The high-dimensional, imbalanced, and distributed data features in virtualized network slicing scenarios invalidate the existing anomaly detection models. Considering the powerful ability of generative adversarial network (GAN) in capturing the distribution from complex data, we design a new multi-discriminator Bidirectional Wasserstein GAN with Gradient Penalty (BiWGAN-GP) model to learn the normal data distribution from high-dimensional resource metrics datasets that are spread on multiple VM monitors. The multi-discriminator BiWGAN-GP model can be trained over distributed data sources, which avoids high communication and computation overhead caused by the centralized collection and processing of local data. We define an anomaly score as the discriminant criterion to quantify the deviation of new metrics data from the learned normal distribution to detect abnormal behaviors arising in VMs. The efficiency and effectiveness of the proposed collaborative anomaly detection algorithm are validated through extensive experimental evaluation on a real-world dataset.

In this paper, we propose two novel cooperative-non-orthogonal-multiple-access (C-NOMA) and cooperative-rate-splitting-multiple-access (C-RSMA) schemes for uplink user cooperation. At the first mini-slot of these schemes, each user transmits its signal and receives the transmitted signal of the other user in full-duplex mode, and at the second mini-slot, each user relays the other user’s message with amplify-and-forward (AF) protocol. At both schemes, to achieve better spectral efficiency, users transmit signals in the non-orthogonal mode in both mini-slots. In C-RSMA, we also apply the rate-splitting method in which the message of each user is divided into two streams. In the proposed detection schemes for C-NOMA and C-RSMA, we apply a combination of maximum-ratio-combining (MRC) and successive-interference-cancellation (SIC). Then, we derive the achievable rates for C-NOMA and C-RSMA, and formulate two optimization problems to maximize the minimum rate of two users by considering the proportional fairness coefficient. We propose two power allocation algorithms based on successive-convex-approximation (SCA) and geometric-programming (GP) to solve these non-convex problems. Next, we derive the asymptotic outage probability of the proposed C-NOMA and C-RSMA schemes, and prove that they achieve diversity order of two. Finally, the above-mentioned performance is confirmed by simulations.

In this paper, we consider spectrum sensing (SS) problems with various general noise models such as Middleton class A (MCA), isometric complex symmetric$\alpha $-stable ($\text{S}\alpha \text{S}$), and isometric complex generalized Gaussian distribution (CGGD). This approach enables us to examine the effect of practical phenomena such as impulsive noise on SS problems. In this general framework, we propose a detector based on convolutional neural networks (CNNs) with favorable performance under various noise models. The proposed model-free and data-driven CNN offers robustness in diverse noise scenarios. Thus, it can be utilized in environments with different physical behaviors. We demonstrate this method outperforms the highly regarded likelihood ratio test (LRT) in most cases. For all impulsive cases, the proposed CNN is the superior detector, providing a near-optimum performance for the conventional Gaussian noise. We indicate the proposed data-driven CNN offers an appropriate alternative solution to LRT. However, it requires more computational operations, a rich training dataset, and a training process, instead. Furthermore, the main rationale for proposing this CNN is that it enables the network to generalize its effective performance to various noise models and cases. To this end, quantitative simulations confirm superiority of the proposed CNN compared to other recent deep-learning methods.

Distributed computing enables Internet of vehicle (IoV) services by collaboratively utilizing the computing resources from the network edge and the vehicles. However, the computing interruption issue caused by frequent edge network handoffs, and a severe shortage of computing resources are two problems in providing IoV services. High altitude platform station (HAPS) computing can be a promising addition to existing distributed computing frameworks because of its wide coverage and strong computational capabilities. In this regard, this paper proposes an adaptive scheme in a new distributed computing framework that involves HAPS computing to deal with the two problems of the IoV. Based on the diverse demands of vehicles, network dynamics, and the time-sensitivity of handoffs, the proposed scheme flexibly divides each task into three parts and assigns them to the vehicle, roadside units (RSUs), and a HAPS to perform synchronous computing. The scheme also constrains the computing of tasks at RSUs such that they are completed before handoffs to avoid the risk of computing interruptions. On this basis, we formulate a delay minimization problem that considers task-splitting ratio, transmit power, bandwidth allocation, and computing resource allocation. To solve the problem, variable replacement and successive convex approximation–based method are proposed. The simulation results show that this scheme not only avoids the negative effects caused by handoffs in a flexible manner, it also takes delay performance into account and maintains the delay stability.

Faster-than-Nyquist (FTN) signaling is a non-orthogonal transmission technique, which has the potential to provide significant spectral efficiency improvement. This paper studies the capacity of FTN signaling for both frequency flat and for frequency selective (FS) multiple-input multiple-output (MIMO) channels. As FTN is another reason of frequency selectivity, we find that precoding in time (or equivalently spectrum shaping in frequency) and waterfilling in spatial domain is capacity achieving for frequency flat MIMO channels with FTN. Meanwhile, waterfilling in both spatial domain and spectrum domain, followed by spectrum shaping, is capacity achieving for FS MIMO channels with FTN.

Reconfigurable Smart Surface (RSS) is assumed to be a key enabler for future wireless communication systems due to its ability to control the wireless propagation environment and, thus, enhance communications quality. Although optimal and continuous phase-shift configuration can be analytically obtained, practical RSS systems are prone to both channel estimation errors, discrete control, and curse of dimensionality. This leads to relaying on a finite number of phase-shift configurations that is expected to degrade the system’s performances. In this paper, we tackle the problem of quantized RSS phase-shift configuration, aiming to maximize the data rate of an orthogonal frequency division multiplexing (OFDM) point-to-point RSS-assisted communication. Due to the complexity of optimally solving the formulated problem, we propose here a sub-optimal greedy algorithm to solve it. Simulation results illustrate the performance superiority of the proposed algorithm compared to baseline approaches. Finally, the impact of several parameters, e.g., quantization resolution and RSS placement, is investigated.

This paper studies the application of neural networks to Viterbi detection of Faster-Than-Nyquist (FTN) signals in an intersymbol interference (ISI) channel. In particular, we propose a novel low-complexity neural network structure for calculating the branch metrics, and we explore its suitability for FTN signalling with channel uncertainty. We compare the proposed network to another neural network-based technique for metric calculation, the ViterbiNet, which was originally designed for ISI channels. The simulation results confirm that the proposed neural network outperforms the ViterbiNet, with much lower complexity, and is much more resilient to channel uncertainty than the traditional Viterbi detector, which uses Euclidean distance for metric calculations. We further show that the proposed neural network exhibits robustness to being trained at mismatched SNR values and FTN squeezing parameters, meaning that the number of trained models required can be significantly reduced. Additionally, the results show that the proposed neural network remains a favorable alternative at much higher levels of channel uncertainties, the results also reflect that we can generalize the proposed network to work with different channel models defined by different decaying factors. Finally, we show that we can still achieve a bandwidth efficiency gain of 33% due to FTN by using the proposed network in the presence of channel uncertainty.

Within the last few years, the multiple-input multiple-output (MIMO) technology has been developing to cope with the stringent 5G and beyond requirements, in terms of high throughput, energy efficiency, and coverage. With the rapid development and miniaturization of Internet-of-Things (IoT) devices, there is an urgent need to design compact-size, low-cost and efficient MIMO antennas for IoT. In this context, we propose here the design of a novel four-element wideband MIMO antenna, supplied using the coplanar waveguide (CPW) technology. The design covers a wide spectrum band between 1.5 and 5.5 GHz, with a total size of 80 × 80 × 1.6 mm3, which is suitable for small IoT devices. The designed MIMO antenna achieves a diversity gain above 9.9 and an envelope correlation coefficient lower than 0.016. Its mutual coupling among any pair of elements is lower than -15 dB, while the group delay demonstrates stability for the targeted band of operation. Hence, the proposed MIMO antenna has a great potential for future wireless communication systems, especially for the sub-6 GHz portable and IoT devices.

With the booming deployment of Internet of Things, health monitoring applications have gradually prospered. Within the recent COVID-19 pandemic situation, interest in permanent remote health monitoring solutions has raised, targeting to reduce contact and preserve the limited medical resources. Among the technological methods to realize efficient remote health monitoring, federated learning (FL) has drawn particular attention due to its robustness in preserving data privacy. However, FL can yield to high communication costs, due to frequent transmissions between the FL server and clients. To tackle this problem, we propose in this paper a communication-efficient federated learning (CEFL) framework that involves clients clustering and transfer learning. First, we propose to group clients through the calculation of similarity factors, based on the neural networks characteristics. Then, a representative client in each cluster is selected to be the leader of the cluster. Differently from the conventional FL, our method performs FL training only among the cluster leaders. Subsequently, transfer learning is adopted by the leader to update its cluster members with the trained FL model. Finally, each member fine-tunes the received model with its own data. To further reduce the communication costs, we opt for a partial-layer FL aggregation approach. This method suggests partially updating the neural network model rather than fully. Through experiments, we show that CEFL can save up to to 98.45% in communication costs while conceding less than 3% in accuracy loss, when compared to the conventional FL. Finally, CEFL demonstrates a high accuracy for clients with small or unbalanced datasets.

In this paper, we propose a cell-free scheme for unmanned-aerial-vehicle (UAV) base-stations (BSs) to manage the severe intercell interference between aerial and terrestrial nodes. Since the cell-free scheme requires a huge bandwidth for backhauling, we propose to use the terahertz (THz) band for the wireless backhaul links between UAV-BSs and central-processing-unit (CPU). Also, because the THz band requires a reliable line-of-sight (LoS) link, instead of a terrestrial CPU, we propose to use a high-altitude-platform-station (HAPS) as a CPU. At the first time-slot of the proposed scheme, users send their messages to UAVs at the sub-6 GHz band. Then each UAV applies match-filtering to align the received signals from users, and performs power allocation for the aligned signal of each user. At the second time-slot, we allocate orthogonal resource-blocks (RBs) for each user at the THz band, and send signals towards HAPS. In HAPS, for aligning the received signals for each user from different UAVs, we perform analog beamforming. Finally, we demodulate and decode the message of each user at its unique RBs. We formulate an optimization problem that maximizes the minimum SINR of users, and find the optimum allocated powers for users in each UAV by the bisection method. Simulation results prove the superiority of the proposed scheme compared with aerial-cellular and terrestrial-cell-free baseline schemes. Simulation results also showed that utilizing HAPS as a CPU is useful when the huge path-loss between UAV-BSs and HAPS in the THz band is compensated by a high number of antennas at HAPS.

Free-space optical satellite networks (FSOSNs) will employ free-space optical links between satellites and between satellites and ground stations, and the link budget for optical inter-satellite links and optical uplink/downlink is analyzed in this paper. The satellites in these FSOSNs will have limited energy and thereby limited power, and we investigate the effect of link distance and link margin on optical inter-satellite link transmission power, and the effect of slant distance, elevation angle, and link margin on optical uplink/downlink transmission power. We model these optical links and compute the results for various parameters. We observe that the transmission power increases when the link distance increases for inter-satellite and uplink/downlink communications, while the transmission power decreases when the elevation angle increases for uplink/downlink transmission. We also observe an inverse relationship between link margin and link distance. Furthermore, we highlight some practical insights and design guidelines gained from this analysis.

Integrating unmanned aerial vehicles (UAVs) as aerial base stations (BSs) into terrestrial cellular networks has emerged as an effective solution to provide coverage and complement communication services in a fast and cost-effective manner. The three-dimensional (3-D) trajectories of UAVs have a remarkable impact on the performance of such networks. On the other hand, UAVs are battery limited, and thus optimizing their energy consumption is of high importance. In this regard, we propose a novel trajectory design mechanism for rotary-wing UAV-BSs in 3-D space to improve the energy efficiency of the network. In this approach, UAVs aim at maximizing an objective function that captures the tradeoff between energy consumption and throughput, while satisfying their ground users’ quality-of-service requirements. Using reinforcement learning, we model our problem as a multiarmed bandit and propose an upper confidence bound-based algorithm to solve the problem. In our proposed mechanism, UAVs autonomously choose their velocities and update their locations adapted to the system conditions without requiring the prior and full knowledge of the system. Simulation results show that our proposed approach yields significant performance gains reaching up to 33.85% in terms of improving the network throughput, and up to 95% of enhancing the energy efficiency compared to a learning-based benchmark. Comparing to a nonlearning-based approach, our proposed approach improves the throughput and energy efficiency by 46.61% and 110%, respectively.

Using unmanned aerial vehicles (UAVs) to cover users in wireless networks has increased in recent years. Deploying UAVs in appropriate positions is important to cover users and nodes properly. In this article, we propose an efficient approach to determine the minimum number of required UAVs and their optimal positions. To this end, we use an iterative algorithm that updates the number of required UAVs at each iteration. To determine the optimal position for the UAVs, we present a mathematical model and solve it accurately after linearizing. One of the inputs of the mathematical model is a set of candidate points for UAV deployments in 2-D space. The mathematical model selects a set of points among candidate points and determines the altitude of each UAV. To provide a suitable set of candidate points, we also propose a candidate point selection method: the MergeCells method. The simulation results show that the proposed approach performs better than the 3-D P-median approach introduced in the literature. We also compare different candidate point selection approaches, and we show that the MergeCells method outperforms other methods in terms of the number of UAVs, user data rates, and simulation time.

The use of satellites to provide ubiquitous coverage and connectivity for densely deployed Internet of Things (IoT) networks is expected to be a reality in emerging 6G networks. Yet the low battery capacity of IoT nodes constitutes a problem for their direct connectivity to satellites, which are located at altitudes of up to 2000 km. In this article, we propose a novel architecture involving the use of reconfigurable intelligent surface (RIS) units to mitigate the path loss associated with long transmission distances. These RIS units can be placed on satellite reflectarrays, and, when used in broadcasting and beamforming, they can provide significant gains in signal transmission. This study shows that RIS-assisted satellites can provide up to 105times higher downlink and achievable uplink rates for IoT networks.

In this work, we propose globally optimal power allocation strategies to maximize the users sum-rate (SR), and system energy efficiency (EE) in the downlink of single-cell multicarrier non-orthogonal multiple access (MC-NOMA) systems. Each NOMA cluster includes a set of users in which the well-known superposition coding (SC) combined with successive interference cancellation (SIC) technique is applied among them. By obtaining the closed-form expression of intra-cluster power allocation, we show that MC-NOMA can be equivalently transformed to a virtual orthogonal multiple access (OMA) system, where the effective channel gain of these virtual OMA users is obtained in closed-form. Then, the SR and EE maximization problems are solved by using very fast water-filling and Dinkelbach algorithms, respectively. The equivalent transformation of MC-NOMA to the virtual OMA system brings new theoretical insights, which are discussed throughout the paper. The extensions of our analyses to other scenarios, such as considering users rate fairness, admission control, long-term performance, and a number of future next-generation multiple access (NGMA) schemes enabling recent advanced technologies, e.g., reconfigurable intelligent surfaces are discussed. Extensive numerical results are provided to demonstrate the performance gaps among single-carrier NOMA (SC-NOMA), OMA-NOMA, and OMA.

We have recently witnessed a number of new software-defined paradigms of VANET in what is referred to as software-defined vehicle networks (SDVN). In order to evaluate the performance of these new proposals and architectures, analytical and simulation models are needed. In this paper, we propose an analytical model based on ON-OFF queueing networks under exponential and general service time distributions. The model can be used to evaluate the performance of SDVNs and takes into account the effect of mobility such as, hand overs, node turning ON/OFF, node going temporary out of coverage, and intermittent connections. This mobility effect was modelled as a queueing station with exponentially random ON-OFF service times, where traffic arrives according to a Poisson random process during the exponentially random ON period and the service time is exponentially distributed. However, during the OFF period the service time is exponentially distributed but with lower rates. We studied the ON-OFF queueing behaviour extensively for both finite-capacity and infinite-capacity queues. Three hypothetical SDVN scenarios were considered, taking into account the effect of mobility and the large number of connected nodes. Results were cross-validated with those obtained by a simulation model. These tools will be valuable for researchers interested in getting quantitative answers for their SDVN architectures.

Network slicing is emerging as a promising method to provide sought after versatility and flexibility to cope with ever-increasing demands. To realize such potential advantages and to meet the challenging requirements of various network slices in an on-demand fashion, we need to develop an agile and distributed mechanism for resource provisioning to different network slices in a heterogeneous multi-resource multi-domain mobile network environment. We formulate inter-domain resource provisioning to network slices in such an environment as an optimization problem which maximizes social welfare among network slice tenants (so that maximizing tenants’ satisfaction), while minimizing operational expenditures for infrastructure service providers at the same time. To solve the envisioned problem, we implement an iterative auction game among network slice tenants, on one hand, and a plurality of price-taking subnet service providers, on the other hand. We show that the proposed solution method results in a distributed privacy-saving mechanism which converges to theoptimal solutionof the described optimization problem. In addition to providing analytical results to characterize the performance of the proposed mechanism, we also employ numerical evaluations to validate the results, demonstrate convergence of the presented algorithm, and show the enhanced performance of the proposed approach (in terms of resource utilization, fairness and operational costs) against the existing solutions.

To provide effective wireless connectivity, the use of aerial vehicles has been proposed as a promising solution in a wide variety of applications. In some of these applications, however, to achieve desirable performance, knowing the location of users may be a necessity. One promising approach for solving the problem of obtaining user locations is through the locations of some nodes (called anchors) and measuring the distance between connected nodes in the network. In this paper, we propose prominent techniques to improve the performance of the localization using multiple moving aerial anchors (MAA). In using MAAs as anchor nodes, the anchor nodes in our scenario have movement capability. This provides us with more flexibility to enhance the accuracy of the localization with taking into consideration the power consumption of MAAs. In fact, we propose an MAA placement method by which each user can be connected to at least one MAA while MAAs communicate with as small as possible power consumption. Then, we propose a path planning for MAAs by which distances between users and MAAs can be measured in order to estimate the locations of users. Our simulation results show that our proposed localization scheme can provide highly accurate location estimations.

Edge intelligence, a new paradigm to accelerate artificial intelligence (AI) applications by leveraging computing resources on the network edge, can be used to improve intelligent transportation systems (ITS). However, due to physical limitations and energy-supply constraints, the computing powers of edge equipment are usually limited. High altitude platform station (HAPS) computing can be considered to be a promising extension of edge computing. HAPS is deployed in the stratosphere to provide wide coverage and strong computational capabilities. It is suitable to coordinate terrestrial resources and store the fundamental data associated with ITS-based applications. In this work, three computing layers, i.e., vehicles, terrestrial network edges, and HAPS, are integrated to build a computation framework for ITS, where the HAPS data library stores the fundamental data needed for the applications. In addition, the caching technique is introduced for network edges to store some of the fundamental data from the HAPS so that large transmission delays can be reduced. We aim to minimize the delay of the system by optimizing computation offloading and caching decisions as well as bandwidth and computing resource allocations. The simulation results highlight the benefits of HAPS computing for mitigating delays and the significance of caching at network edges.

High altitude platform station (HAPS) computing can be considered as a promising extension of edge computing to improve intelligent transportation systems (ITS). HAPS is deployed in the stratosphere to provide wide coverage and strong computational capabilities, which is suitable to coordinate terrestrial resources and store the fundamental data associated with ITS-based applications. In this work, three computing layers, i.e., vehicles, terrestrial network edges, and HAPS, are integrated to build a computation framework for ITS, where the HAPS data library stores the fundamental data needed for the applications. In addition, the caching technique is introduced for network edges to store some of the fundamental data from the HAPS so that large propagation delays can be reduced. We aim to minimize the delay of the system by optimizing computation offloading and caching decisions as well as bandwidth and computing resource allocations. The simulation results highlight the benefits of HAPS computing for mitigating delays and the significance of caching at network edges.

On-board battery consumption, cellular disconnectivity, and frequent handoff are key challenges for unmanned aerial vehicle (UAV) based delivery missions, a.k.a., cargo-UAV. Indeed, with the introduction of UAV technology into cargo shipping and logistics, designing energy-efficient paths becomes a serious issue for the next retail industry transformation. Typically, the latter has to guarantee uninterrupted or slightly interrupted cellular connectivity for the UAV’s command and control through a small number of handoffs. In this paper, we formulate the trajectory planning as a multi-objective problem aiming to minimize both the UAV’s energy consumption and the handoff rate, constrained by the UAV battery size and disconnectivity rate. Due to the problem’s complexity, we propose a dynamic programming based solution. Through simulations, we demonstrate the efficiency of our approach in providing opti-mized UAV trajectories. Also, the impact of several parameters, such as the cargo-UAV altitude, disconnectivity rate, and type of environment, are investigated. The obtained results allow to draw recommendations and guidelines for cargo-UAV operations.

We address the mobility management of an autonomous UAV-mounted base station (UAV-BS) that provides communication services to a cluster of users on the ground while the geographical characteristics (e.g., location and boundary) of the cluster, the geographical locations of the users, and the characteristics of the radio environment are unknown. UAV-BS solely exploits the received signal strengths (RSS) from the users and accordingly chooses its (continuous) 3-D speed to constructively navigate, Le., improving the transmitted data rate. To compensate for the lack of a model, we adopt policy gradient deep reinforcement learning. As our approach does not rely on any particular information about the users as well as the radio environment, it is flexible and respects the privacy concerns. Our experiments indicate that despite the minimum available information the UAV-BS is able to distinguish between high-rise (often non-line-of-sight dominant) and sub-urban (mainly line-of-sight dominant) environments such that in the former (resp. latter) it tends to reduce (resp. increase) its height and stays close (resp. far) to the cluster. We further observe that the choice of the reward function affects the speed and the ability of the agent to adhere to the problem constraints without affecting the delivered data rate.

Power allocation in spectrum sharing systems is challenging due to excessive interference that the secondary system could impose on the primary system. Therefore, an interference threshold constraint is considered to regulate the secondary system's activity. However, the primary receivers should measure the interference and inform the secondary users accordingly. These cause design complexities, e.g., due to transceiver's hardware impairments, and impose a substantial signaling overhead. We set our main goal to mitigate these requirements in order to make the spectrum sharing systems practically feasible. To cope with the lack of a model we develop a coexisting deep reinforcement learning approach for continuous power allocation in both systems. Importantly, via our solution, the two systems allocate power merely based on geographical location of their users. Moreover, the inter-system signalling requirement is reduced to exchanging only the number of primary users that their QoS requirements are violated. We observe that compared to a centralized agent that allocates power based on full (accurate) channel information, our solution is more robust and strictly guarantees QoS requirements of the primary users. This implies that both systems can operate simultaneously with almost-zero inter-system signaling overhead.

The paper studies the downlink of non-orthogonal multiple access (NOMA) under imperfect successive interference cancellation (SIC). In an effort to overcome the SIC imperfection problem, we propose to adopt improper Gaussian signaling (IGS) for the signal transmission of the NOMA users. In particular, we jointly optimize the IGS circularity coefficient and transmit power to maximize the spectral efficiency subject to minimum Quality-of-Service (QoS) per each user and total power budget. Simulations results show the effectiveness of IGS to compensate for the SIC imperfections compared with proper Gaussian signaling (PGS)-based NOMA systems.

In this paper, graph attention network (GAT) is firstly utilized for the channel estimation. In accordance with the 6G expectations, we consider a high-altitude platform station (HAPS) mounted reconfigurable intelligent surface-assisted two-way communications and obtain a low overhead and a high normalized mean square error performance. The performance of the proposed method is investigated on the two-way backhauling link over the RIS-integrated HAPS. The simulation results denote that the GAT estimator overperforms the least square in full-duplex channel estimation. Contrary to the previously introduced methods, GAT at one of the nodes can separately estimate the cascaded channel coefficients. Thus, there is no need to use time division duplex mode during pilot signaling in full-duplex communication. Moreover, it is shown that the GAT estimator is robust to hardware imperfections and changes in small scale fading characteristics even if the training data do not include all these variations.

Ultra-reliable low-latency communication (URLLC) requires short packets of data transmission. It is known that when the packet length becomes short, the achievable rate is subject to a penalty when compared to the channel capacity. In this paper, we propose to use faster-than-Nyquist (FTN) signaling to compensate for the achievable rate loss of short packet communications. We investigate the performance of a combination of a low complexity detector of FTN signaling used with nonbinary low-density parity-check (NB-LDPC) codes that is suitable for low-latency and short block length requirements of URLLC systems. Our investigation shows that such combination of low-complexity FTN signaling detection and NB-LDPC codes outperforms the use of close-to-optimal FTN signaling detectors with LDPC codes in terms of error rate performance and also has a considerably lower computational complexity.

Terahertz (THz) communication is gaining more interest as one of the envisioned enablers of high-data-rate short-distance indoor applications in beyond 5G networks. Moreover, non-orthogonal multiple-access (NOMA)-enabled schemes are promising schemes to realize the target spectral efficiency, low latency, and user fairness requirements in future networks. In this paper, an energy-efficient cooperative NOMA (CNOMA) scheme that guarantees the minimum required rate for the cell-edge users in an indoor THz-MISO communications network is proposed. The proposed cooperative scheme consists of three stages: (i) beamforming stage that allocates base-station (BS) beams to THz cooperating cell-center users using analog beamforming with the aid of the cosine similarity metric, (ii) user pairing stage that is tackled using the Hungarian algorithm, and (iii) power allocation stage for both the BS THz-NOMA transmit power and the cooperation power of the cooperating cell-center users, which are optimized sequentially. The obtained results quantify the energy efficiency (EE) of the proposed scheme and shed new light on the performance of multi-user THz-NOMA-enabled networks.

Due to recent technological advancements in the area of unmanned aerial systems, equipping an unmanned aerial vehicle (UAV) with a base station (BS) has been proposed to augment terrestrial base stations and to enhance the performance of 5G and beyond-5G networks. In this paper, we investigate the 3D placement problem for multiple UAV-BSs that maximizes the number of covered users with the same as well as with different quality-of-service (QoS) requirements. First, we present a mathematical formulation of the multiple UAV-BS placement problem, and show that it is a non-convex optimization problem. Then, we propose two heuristic algorithms, and we show that for users with the same as well as with different QoS requirements, the proposed algorithms outperform the Linear Approximation (LA) state-of the-art algorithm in terms of the average number of covered users and execution time, and achieve near optimal performance. Finally, a tradeoff between the two heuristic algorithms in terms of the average number of covered users and their execution time is presented.

The widespread use of new technologies, such as the Internet of Things and machine-type communication (MTC) forces an increase on the number of user equipments (UEs) and MTC devices that are connecting to mobile networks. Inherently, as the number of UEs inside a base station’s (BSs) coverage area surges, the quality of service tends to decline. The use of drone-BS (UxNB) is a solution in places where UEs are densely populated, such as stadiums. UxNB emerges as a promising technology that can be used for capacity injection purposes in the future due to its fast deployment. However, this emerging technology introduces a new security issue. Mutual authentication, creating a communication channel between terrestrial BS and UxNB, and fast handover operations may cause security issues in the use of UxNB for capacity injection. This new protocol also suggests performing UE handover from terrestrial to UxNB as a group. To the best of our knowledge, there is no authentication solution between BSs according to LTE and 5G standards. The proposed scheme provides a solution for the authentication of UxNB by the terrestrial BS. Additionally, a credential sharing phase for each UE in handover is not required in the proposed method. The absence of a credential sharing step saves resources by reducing the number of communications between BSs. Moreover, many UE handover operations are completed in concise time within the proposed group handover method.

In this article, we aim to sustain unmanned aerial vehicle (UAV)-based missions for longer periods of times through different techniques. First, we consider on-the-mission UAV recharging by a low-power laser source (below 1 kW). In order to achieve the maximal energy gain from the low-power laser source, we propose an operational compromise, which consists of the UAV resting over buildings with cleared line of sight to the laser source. Second, to provide a precise energy consumption/harvesting estimation at the UAV, we investigate the latter's dynamics in a mission environment. Indeed, we study the UAV's battery dynamics by leveraging the electrical models for motors and battery. Subsequently, using these models, the path planning problem in a particular Internet-of-Things-based usecase is revisited from the battery perspective. The objective is to extend the UAV's operation time using both laser-charging and accurate battery level estimation. Through a graph theory approach, the problem is solved optimally, and compared to benchmark trajectory approaches. Numerical results demonstrate the efficiency of this novel battery perspective for all path planning approaches. In contrast, we found that the energy perspective is very conservative and does not exploit optimally the available energy resources. Nevertheless, we propose a simple adjustment method to correct the energy perspective, by carefully evaluating the energy as a function of the UAV motion regimes. Finally, the impact of several parameters, such as turbulence and distance to charging source, is studied.

To cope with the unprecedented surge in demand for data computing for the applications, the promising concept of multiaccess edge computing (MEC) has been proposed to enable the network edges to provide closer data processing for mobile devices (MDs). Since enormous workloads need to be migrated, and MDs always remain resource-constrained, data offloading from devices to the MEC server will inevitably require more efficient transmission designs. The integration of nonorthogonal multiple access (NOMA) technique with MEC has been shown to provide applications with lower latency and higher energy efficiency. However, the existing designs of this type have mainly focused on the transmission technique, which is still insufficient. To further advance offloading performance, in this work, we propose an application-driven NOMA-enabled computation offloading scheme by exploring the characteristics of applications, where the common data of the application is offloaded through multidevice cooperation. Under the premise of successfully offloading the common data, we formulate the problem as the maximization of individual offloading throughput, where the time allocation and power control are jointly optimized. By using the successive convex approximation (SCA) method, the formulated problem can be iteratively solved. Simulation results demonstrate the convergence of our method and the effectiveness of the proposed scheme.

Next-generation wireless networks are expected to be highly heterogeneous, multilayered, with embedded intelligence at both the core and edge of the network. In such a context, system-level performance evaluation will be very important to formulate relevant insights into tradeoffs that govern such a complex system. Over the past decade, SG has emerged as a powerful analytical tool to evaluate the system-level performance of wireless networks and capture their tendency toward heterogeneity. However, with the imminent onset of this crucial new decade, where global commercialization of fifth generation (5G) is expected to emerge and essential research questions related to beyond 5G (B5G) are intended to be identified, we are wondering about the role that a powerful tool, such as SG, should play. In this article, we first aim to track and summarize the novel SG models and techniques developed during the last decade in the evaluation of wireless networks. Next, we will outline how SG has been used to capture the properties of emerging RANs for 5G/B5G and quantify the benefits of key enabling technologies. Finally, we will discuss new horizons that will breathe new life into the use of SG in the foreseeable future, for instance, using SG to evaluate performance metrics in the visionary paradigm of molecular communications. Also, we will review how SG is envisioned to cooperate with machine learning that is seen as a crucial component in the race toward ubiquitous wireless intelligence. Another important insight is Grothendieck's toposes, which is considered as a powerful mathematical concept that can help to solve long-standing problems formulated in SG.

The key role of spectrum sensing in cognitive radios attracted substantial research attention to improve the performance of detectors. We consider a general model for the receiver noise with the potential of generalization towards modeling noise in various environments. This general noise model describes accurately noise characteristics ranging from the Gaussian noise to the severe impulsive noise. However, many previous studies are based on the ideal Gaussian noise, and they cannot capture the non-Gaussian models. To provide a robust detector against different behaviors of the noise in various environments, we employ a convolutional neural network (CNN) compatible with different noise models. The proposed CNN detector is data-driven, and due to its single-dimensional input layer, it is consistent with the received signal and requires no pre-processing. The likelihood ratio test (LRT), the Wald, and the Rao tests for this problem are derived to enrich the paper with comparative evaluations of the proposed CNN and conventional model-based approaches and other neural networks. Although various simulated scenarios substantiate the general superiority and robustness of the CNN-based against impulsive noise and mismatch of parameters, it requires higher computational complexity than other discussed detectors.

In this paper, we consider a cooperative multiple-input single-output (MISO) heterogeneous communication network based on the power domain non-orthogonal multiple access (PD-NOMA). We aim to investigate a resource allocation problem regarding the uncertainty of the channel state information at the transmitter (CSIT) and the imperfect SIC case. Since there is an essential need for low-complexity algorithms with reasonably good performance for the extremely complex access architectures, we propose two novel methods based on matching game with externalities and successive convex approximation (SCA) to realize the hybrid scheme where the number of the cooperative nodes is variable. Moreover, we propose a new matching utility function to manage the interference caused by cooperative networks and PD-NOMA. We also devise two robust beamforming techniques to cope with the channel uncertainty based on the worst-case and stochastic-case scenarios. Simulation results evaluate the performance and the sensibility of the proposed methods and demonstrate that although the performance of the proposed distributed matching algorithm is slightly inferior to that of the SCA type, the complexity of the matching theory approach is substantially lower than that of the latter one.

Designing multidimensional constellations (MdCs) is an integral part of sparse code multiple access (SCMA). Since the optimal maximum a posteriori (MAP) receiver for SCMA is too complex in most applications, one highly popular technique is the near-optimal message passing algorithm (MPA), where its performance improves with increasing the signal-to-noise ratio (SNR) and the number of iterations. When the number of MPA iterations has to be limited (e.g., low-latency and/or low-complexity and/or energy-sensitive applications), the performance gap between MAP and MPA becomes significant, especially at low-to-medium SNRs. Inspired by the promising features of hypercubes when used along with bit-interleaved coded modulation, we construct novel MdCs which are based on a unitary rotation of a hypercube by a rotation angle that aims to achieve the minimum frame-error-rate (FER). By exploiting special properties of hypercubes, we fit a second-order rational polynomial to a few measured FER samples, and find a close-to-optimal rotation angle at each SNR and MPA iteration. Our proposed MdCs provide substantial performance gains (as much as 2 dB) in comparison to the best known SCMA MdCs in the literature, especially in low-to-medium SNR regions when the number of MPA iterations has to be low, and in the presence of 5G-compliant LDPC codes.

In this paper, we analyze the downlink coverage probability and rate of an aerial user in vertical HetNets (VHetNets) comprising aerial base stations (aerial-BSs) and terrestrial-BSs. The locations of terrestrial-BSs are modeled as an infinite 2-D Poisson Point Process (PPP), while the locations of aerial-BSs are modeled as a finite 2-D Binomial Point Process (BPP). Our cellular-to-air (C2A) channel model incorporates line-of-sight (LoS) and non-LoS transmissions between terrestrial-BSs and a typical aerial user, while we assume LoS transmissions for all aerial links. We assume that the aerial user is associated with an aerial-BS or terrestrial-BS that provides the strongest average received power. Using stochastic geometry, we derive exact and approximate expressions of the coverage probability and rate in terms of interference power's Laplace transform. The expressions are simplified assuming only LoS transmissions for the C2A channels. This enables easy-to-compute equations with good accuracy at elevated aerial user heights. We find that aerial users hovering at low altitudes tend to connect to aerial-BSs in denser terrestrial environments. Employing directive beamforming at aerial-BSs guarantees an acceptable performance at the aerial user by reducing interference signals received from the aerial-BSs. In denser terrestrial networks, the performance at the aerial user degrades substantially despite beamforming.

In this paper, we aim at interference mitigation in 5G millimeter-Wave (mm-Wave) communications by employing beamforming and Non-Orthogonal Multiple Access (NOMA) techniques with the aim of improving network's aggregate rate. Despite the potential capacity gains of mm-Wave and NOMA, many technical challenges might hinder that performance gain. In particular, the performance of Successive Interference Cancellation (SIC) diminishes rapidly as the number of users increases per beam, which leads to higher intra-beam interference. Furthermore, intersection regions between adjacent cells give rise to inter-beam inter-cell interference. To mitigate both interference levels, optimal selection of the number of beams in addition to best allocation of users to those beams is essential. In this paper, we address the problem of joint user-cell association and selection of number of beams for the purpose of maximizing the aggregate network capacity. We propose three machine learning-based algorithms; transfer Q-learning (TQL), Q-learning, and Best SINR association with Density-based Spatial Clustering of Applications with Noise (BSDC) algorithms and compare their performance under different scenarios. Under mobility, TQL and Q-learning demonstrate 12% rate improvement over BSDC at the highest offered traffic load. For stationary scenarios, Q-learning and BSDC outperform TQL, however TQL achieves about 29% convergence speedup compared to Q-learning.

Unmanned aerial vehicles (UAVs) can be users that support new applications, or be communication access points that serve terrestrial and/or aerial users. In this paper, we focus on the connectivity problem of aerial users when they are exclusively served by aerial base stations (BS), i.e., UAV-BSs. Specifically, the 3D placement problem of a directional-antenna equipped UAV-BS, aiming to maximize the number of covered aerial users under a spectrum sharing policy with terrestrial networks, is investigated. Given a known spectrum sharing policy between the aerial and terrestrial networks, we propose a 3D placement algorithm that achieves optimality. Simulation results show the performance of our approach, in terms of number of covered aerial users for different configurations and parameters, such as the spectrum sharing policy, antenna beamwidth, transmit power, and aerial users density. These results represent novel guidelines for exclusive aerial networks deployment and applications, distinctively for orthogonal and non-orthogonal spectrum sharing policies with terrestrial networks.

In this paper, we focus on energy-efficient UAV-based IoT data collection in sensor networks in which the sensed data have different time deadlines. In the investigated setting, the sensors are clustered and managed by cluster heads (CHs), and multiple UAVs are used to collect data from the CHs. The formulated problem is solved through a two-step approach. In the first step, an efficient method is proposed to determine the minimal number of CHs and their best locations. Subsequently, the minimal number of UAVs and their trajectories are obtained by solving the associated capacitated vehicle routing problem. Results show the efficiency of our proposed CHs placement method compared to baseline approaches, where bringing the CHs closer to the dockstation allows significant energy savings. Moreover, among different UAV trajectory planning algorithms, Tabu search achieves the best energy consumption. Finally, the impact of the battery capacity and time deadline are investigated in terms of consumed energy, number of visited CHs, and number of deployed UAVs.

In this paper, we characterize, based on stochastic geometry, the uplink coverage probability with a unified power control scheme built upon realistic path loss models and user equipment (UE) constrained transmit power. To improve their uplink connectivity, active UEs are next assumed to move in a random direction without prior knowledge of their nearest base station location, namely the blind cell search (BCS) movement. A tractable expression of the uplink handoff rate is then derived and the induced uplink coverage probability following the BCS movement is evaluated. The results show different echoes of the uplink coverage probability depending on the serving UE profile (stationary or mobile) and the considered path loss model, which suggests new insights into the design of uplink system parameters.

In this paper, we find trajectory planning and power allocation for a vehicular network in which an unmanned-aerial- vehicle (UAV) is considered as a relay to extend coverage for two disconnected far vehicles. We show that in a two-user network with an amplify-and-forward (AF) relay, non-orthogonal- multiple-access (NOMA) always has better or equal sum-rate performance in comparison to orthogonal-multiple-access (OMA) at high signal-to-noise-ratio (SNR) regime. However, for the cases where i) base station (BS)-to-relay link is weak, or ii) two users have similar links, or iii) BS-to-relay link is similar to relay-to-weak user link, applying NOMA has negligible sum-rate gain. Hence, due to the complexity of successive-interference- cancellation (SIC) decoding in NOMA, we propose a dynamic NOMA/OMA scheme in which the OMA mode is selected for transmission when applying NOMA has only negligible gain. Further, we formulate an optimization problem that maximizes the sum-rate of the two vehicles. This problem is non-convex, and hence we propose an iterative algorithm based on alternating- optimization (AO) method which solves trajectory and power allocation sub-problems by successive-convex-approximation (SCA) and difference-of-convex (DC) methods, respectively. Finally, the above-mentioned performance is confirmed by simulations.

Wireless network personalization is an emerging technology that has considerable potential to achieve the ultimate balance between resource allocation and user satisfaction. One of the main enablers of personalized networks is the continuous monitoring and prediction of dynamic user satisfaction levels in various contexts. Accurate satisfaction prediction requires a lot of data, and unfortunately, data and the process of acquiring it are expensive. A closer look at user behavior and satisfaction levels reveal that certain users share certain behavioral similarities. A group of users who share similar user behavior and satisfaction patterns is referred to as a persona. Associating users with preexisting user personas will enable networks to provide highly personalized services with a minimal amount of data, thereby improving the efficiency of personalized networks. In this paper, we propose a novel big data-driven framework to predict user personas in personalized wireless networks. Also, we conduct a comprehensive study to investigate the impact of different amounts of data and confidence levels on the performance of the overall framework. Finally, using a simulated personalized wireless network, we compare the behavior of different personas in terms of the amount of saved resources and achieved satisfaction levels.

Reduced complexity faster-than-Nyquist (FTN) signaling systems are gaining increased attention as they provide improved bandwidth utilization for an acceptable level of detection complexity. In order to have a better understanding of the tradeoff between performance and complexity of the reduced complexity FTN detection techniques, it is necessary to study these techniques in the presence of channel coding. In this paper, we investigate the performance a polar coded FTN system which uses a reduced complexity FTN detection, namely, the recently proposed “successive symbol-by-symbol with go-back- K sequence estimation (SSSgbKSE)” technique. Simulations are performed for various intersymbol-interference (ISI) levels and for various go-back-K values. Bit error rate (BER) performance of Bahl-Cocke-Jelinek-Raviv (BCJR) detection and SSSgbKSE detection techniques are studied for both uncoded and polar coded systems. Simulation results reveal that polar codes can compensate some of the performance loss incurred in the reduced complexity SSSgbKSE technique and assist in closing the performance gap between BCJR and SSSgbKSE detection algorithms.

Sparse code multiple access (SCMA) is a potential non-orthogonal multiple access candidate for future wireless systems. The key performance indicators (KPIs) of uplink SCMA multidimensional constellations (MdCs) that should be considered in their design process have recently been identified in conjunction with the LTE turbo code for different channel scenarios. However, it is questionable whether the same KPIs are applicable to designing MdCs when a different error correcting code is employed. In this paper, we investigate the effect of the high-rate and low-rate 5G low density parity check (LDPC) codes on determining KPIs in designing MdCs for uplink SCMA systems under various channel scenarios. Through simulations, we show that similar results to the LTE turbo coded case occur in the presence of 5G LDPC code, with one notable exception over one specific scenario. The exception is in the performance of one MdC, which has a low number of distinct points; its performance is significantly worse than predicted by the KPIs when the low-rate 5G LDPC code is employed. This phenomenon happens due to the inherent structure of the 5G LDPC code, in which we propose a pseudorandom interleaver to rectify the problem.

Internet-of-Things (IoT) networks are becoming a part of our daily lives, as the number of IoT devices around us are surging. The authentication of millions of connected things and the distribution and management of secret keys between these devices pose challenging research problems. Current one-to-one authentication schemes do not take the resource limitations of IoT devices into consideration. Nor do they address the scalability problem of massive machine type communication (mMTC) networks. Group authentication schemes (GASs), on the other hand, have emerged as novel approaches for many-to-many authentication problems. They can be used to simultaneously authenticate numerous resource-constrained devices. However, existing GAS is not energy efficient and they do not provide enough security for widespread use. In this article, we propose a lightweight GAS that significantly reduces energy consumption on devices, providing almost 80% energy savings when compared to the state-of-the-art solutions. Our approach is also resistant to the replay and man-in-the-middle attacks. The proposed approach also includes a solution for key agreement and key distribution problems in mMTC environments. Moreover, this approach can be used in both centralized and decentralized group authentication scenarios. The proposed approach has the potential to address the fast authentication requirements of the envisioned agile 6G networks, supported through aerial networking nodes.

In this paper, we propose a novel framework for 5G and beyond (5G+) heterogeneous wireless networks consisting of macro aerial base stations (MABSs), small aerial base stations (SABSs), and ground base stations (GBSs) with two types of access technologies: power domain non-orthogonal multiple access (PD-NOMA) and orthogonal frequency-division multiple access (OFDMA). We aim to maximize the total network profit under some practical network constraints, e.g., NOMA and OFDMA limitations, transmit power (TP) maximum limits, and isolation of the virtualized wireless network. We formulate the resource allocation problem encompassing joint TP allocation, ABS altitude determination, user association, and sub-carrier allocation parameters. Our optimization problem is mixed integer non-linear programming (MINLP) with high computational complexity. To propose a practical approach with reduced computational complexity, we use an alternate method where the main optimization is broken down into three sub-problems with lower computational complexity. We do this by adopting successive convex approximation (SCA), geometric programming (GP), and mesh adaptive direct search (MADS) to solve each of the resulting problems, and find power allocation, altitudes of ABSs, and assignment parameters, respectively. Simulation results reveal that our proposed scenario can improve the overall network profit by up to 47 percent compared to the case where the TPs and ABS altitudes are fixed. Besides, finding the ABS altitude with fixed TPs can improve the network profit by 20 percent compared to the power allocation case with fixed ABS altitudes. Our proposed heterogeneous approach improves the network profit by up to 18, 16, 15, and 10 percent in suburban, urban, dense urban, and high-rise urban environments, respectively, compared to the cases with homogeneous ABSs.

In this paper, we find trajectory planning and power allocation for a vehicular network in which an unmanned-aerial-vehicle (UAV) is considered as a relay to extend coverage for two disconnected far vehicles. We show that in a two-user network with an amplify-and-forward (AF) relay, non-orthogonal-multiple-access (NOMA) always has better or equal sum-rate in comparison to orthogonal-multiple-access (OMA) at high signal-to-noise-ratio (SNR) regime. However, for the cases where i) base station (BS)-to-relay link is weak, or ii) two users have similar links, or iii) BS-to-relay link is similar to relay-to-weak user link, applying NOMA has negligible sum-rate gain. Hence, due to the complexity of successive-interference-cancellation (SIC) decoding in NOMA, we propose a dynamic NOMA/OMA scheme in which OMA mode is selected for transmission when applying NOMA has only negligible gain. Also, we show that OMA always has better min-rate than NOMA at high SNR regime. Further, we formulate two optimization problems which maximize the sum-rate and min-rate of the two vehicles. These problems are non-convex, and hence we propose an iterative algorithm based on alternating-optimization (AO) method which solves trajectory and power allocation sub-problems by successive-convex-approximation (SCA) and difference-of-convex (DC) methods, respectively. Finally, the above-mentioned performance is confirmed by simulations.

Use of aerial base stations (ABSs) is a promising approach to enhance the agility and flexibility of future wireless networks. ABSs can improve the coverage and/or capacity of a network by moving supply towards demand. Deploying ABSs in a network presents several challenges such as finding an efficient 3D-placement of ABSs that takes network objectives into account. Another challenge is the limited wireless backhaul capacity of ABSs and consequently, potentially higher latency incurred. Content caching is proposed to alleviate the backhaul congestion and decrease the latency. We consider a limited backhaul capacity for ABSs due to varying position-dependent path loss values and define two groups of users (delay-tolerant and delay-sensitive) with different data rate requirements. We study the problem of jointly determining backhaul-aware 3D placement for ABSs, user-BS associations and corresponding bandwidth allocations while minimizing total downlink transmit power. Proposed iterative algorithm applies a decomposition method. First, the 3D locations of ABSs are found using semi-definite relaxation and coordinate descent methods, and then user-BS associations and bandwidth allocations are optimized. The simulation results demonstrate the effectiveness of the proposed algorithm and provide insights about the impact of traffic distribution and content caching on transmit power and backhaul usage of ABSs.

In this paper, we propose a framework design for wireless sensor networks based on multiple unmanned aerial vehicles (UAVs). Specifically, we aim to minimize deployment and operational costs, with respect to budget and power constraints. To this end, we first optimize the number and locations of cluster heads (CHs) guaranteeing data collection from all sensors. Then, to minimize the data collection flight time, we optimize the number and trajectories of UAVs. Accordingly, we distinguish two trajectory approaches: 1) where a UAV hovers exactly above the visited CH; and 2) where a UAV hovers within a range of the CH. The results of this include guidelines for data collection design. The characteristics of sensor nodes' K-means clustering are then discussed. Next, we illustrate the performance of optimal and heuristic solutions for trajectory planning. The genetic algorithm is shown to be near- optimal with only 3:5% degradation. The impacts of the trajectory approach, environment, and UAVs' altitude are investigated. Finally, fairness of UAVs trajectories is discussed.

Ubiquitous wireless connectivity is critical for the operation of aerial users. In this paper, we investigate the downlink coverage probability of a typical aerial user served by a network of terrestrial-BSs and aerial-BSs, which we refer to as the vertical HetNet (VHetNet). We model the terrestrial- BSs as an infinite 2-D Poisson point process (PPP) and aerial-BSs as a finite 2-D Binomial point process (BPP). A typical aerial user is assumed to be associated with the BS that provides the strongest biased average received signal power. Closed-form expressions for the association probabilities to a terrestrial-BS and an aerial-BS are derived. Under the assumption of an interference-limited network, we derive an expression for the coverage probability in terms of the Laplace transform of the aggregate interference power. Several design insights are presented, showing that the coverage probability does not have a steady behavior with the aerial user height; and a denser aerial-BS network improves the overall coverage probability. Moreover, the results reveal that the coverage probability under unbiased aerial user association always outperforms its biased counterpart.

Current wireless networks are over-provisioned in order to maintain an average acceptable user experience for most users on the network. Over-provisioned networks suffer from several issues, however, including network inefficiency and the inability to maintain a certain user satisfaction level for all users. Data-driven wireless network personalization is proposed as a dynamic context-aware approach to maintaining the targeted personalized satisfaction levels with minimum resources. Wireless network personalization has two key enablers: measuring and predicting user satisfaction in real-time, and datasets that have both context and user satisfaction information. In this paper, we first present the Zone of Tolerance (ZoT) concept, which is proposed for modeling the relationship between context, service performance, and user satisfaction. Then, since datasets for user behavior and their corresponding satisfaction levels do not exist due to privacy and confidentiality concerns, we propose a process based on the ZoT model for synthesizing a context-based dataset along with its corresponding user satisfaction values. Finally, an exemplary user satisfaction prediction experiment is conducted with the generated dataset using several Machine Learning (ML) algorithms.

The recent evolution of ambient backscattering technology has the potential to provide long-range and low-power wireless communications. In this work, we study the unmanned aerial vehicle (UAV)-assisted backscatter networks where the UAV acts both as a mobile power transmitter and as an information collector. We aim to maximize the number of successfully decoded bits in the uplink while minimizing the UAV's flight time by optimizing its altitude. Power-domain NOMA scheme is employed in the uplink. An optimization framework is presented to identify the trade-off between numerous network parameters, such as UAV's altitude, number of backscatter devices, and backscatter coefficients. Numerical results show that an optimal altitude is computable for various network setups and that the impact of backscattering reflection coefficients on the maximum network throughput is significant. Based on this optimal altitude, we also show that an optimal trajectory plan is achievable.

Analysis of the performance of Unmanned Aerial Vehicle (UAV)-enabled communications systems often relies upon idealized antenna characteristic, where the side-lobe gain of UAVs' antenna is ignored. In practice, however, side-lobe cause inevitable interference to the ground users. We investigate the impact of UAVs' antenna side-lobe on the performance of UAV-enabled communication. Our analysis shows that even for a very small antenna's side-lobe gain, the ground receiver can experience substantial interference. We further show that a rather large exclusion zone is required to ensure a sufficient level of protection for the ground receiver. Nevertheless, in a multiple-antenna setting for the ground users, even when such a large exclusion zone was in place, UAVs' antenna side-lobe creates a high level of correlation among the interference signals received across receive antennas. Such a correlation limits the system ability to exploit channel diversity in a multiple-antenna setting for improving capacity. We then quantify the impact of UAVs' antenna side-lobes on the overall system performance by deriving the corresponding loss of the achieved capacity in various communications environments. We provide a new quantitative insight on the cost of adopting non-ideal UAV antenna on the overall capacity. Our analysis also shows that the capacity loss can be confined by careful selection of system parameters.

In a cost-effective implementation, we address the problem of cross-interference in cognitive multiple-input multiple-output (MIMO) spectrum sharing where mutual cochannel interference (CCI) between the primary and secondary systems is of major concern. First, we propose an optimal transmit-antenna selection (TAS) strategy that rather maximizes the received signal-to-interference-plus-noise ratio (SINR) after maximum-ratio combining (MRC). We statistically analyze the combined SINR structure and derive new expressions of its cumulative and probability distribution functions. Interestingly, we accurately show the substantial gains offered by the proposed TAS strategy in terms of the secondary system outage performance compared to the existent state-of-the-art TAS strategies. Finally, our analytical results are validated by simulations and some important insights are also underlined.

This paper studies a novel dual-mode scheduling framework that jointly performs power allocation, beamforming, and sparse code multiple access (SCMA) based scheduling over microwave and millimeter wave (mmW) bands. We propose a robust secure transmission scheme assuming imperfect channel state information for the eavesdropper links. The proposed scheduling framework allows users to schedule simultaneously on each dual-mode BS, based on SCMA, to maximize the joint access secrecy and backhaul rates under transmit power constraints. It is shown that the proposed scheduling framework can find an effective scheduling solution over both microwave and mmW in polynomial time. Simulation results show that the dual connectivity, and joint solutions have 22.5%, and 20% performance gain compared to only microwave, and the disjoint solution, respectively.

Providing seamless coverage in current cellular network technologies is surmountable only through gross overengineering. Alternatively, as an economically effective solution, the use of unmanned aerial vehicles (UAVs), augmented with the functionalities of terrestrial base stations (BSs), is recently advocated. In this paper we investigate the effect that the incorporation of UAV-mounted BSs (U-BS) poses on the coverage probability of cellular networks. To this end, we focus on the evaluation of the coverage probability of a large-scale aerialterrestrial heterogenous cellular network (AT-HetNet), in which BSs of each technology/tier can be either ground (G-BS) or UBS. Our analysis incorporates the impact of Line-of-Sight (LOS) and non-LOS (NLOS) path-loss attenuations of both ground-toground (G2G) and Air-to-Ground (A2G) links. Adopting tools of stochastic geometry we then obtain the coverage probability as a function of main system parameters and percentage of BSs in each tier that are aerial. Using simulations we also confirm the accuracy of our analysis. We further observe that for several common communication environments, e.g., high-rise and dense urban environments, the inclusion of U-BSs can be detrimental to the coverage probability. Nevertheless, it is still possible to minimize the coverage cost by turning off a percentage of G-BSs. Interestingly, for urban and sub-urban areas one can adjust the altitude of U-BSs in order to adjust the coverage probability.

Caching the content closer to the user equipments (UEs) in heterogenous cellular networks (HetNets) improves user-perceived Quality-of-Service (QoS) while lowering the operators backhaul usage/costs. Nevertheless, under the current networking strategy that promotes aggressive densification, it is unclear whether cache-enabled HetNets preserve the claimed cost-effectiveness and the potential benefits. This is due to 1) the collective cost of caching which may inevitably exceed the expensive cost of backhaul in a dense HetNet, and 2) the excessive interference which affects the signal reception irrespective of content placement. We analyze these significant, yet overlooked, issues, showing that while densification reduces backhaul load and increases spectral efficiency in cache-enabled dense networks, it simultaneously reduces cache-hit probability and increases the network cost. We then introduce a caching efficiency metric, area spectral efficiency per unit spent cost, and find it enough to cache only about 3% of the content library size in the cache of small-cell base stations. We further show that range expansion, known to be of substantial value in wireless networks, is almost impotent to curb the caching inefficiency. Surprisingly, unlike the conventional wisdom recommending traffic offloading from macro cells to small cells, in cache-enabled HetNets, it is more beneficial to exclude offloading altogether or to do the opposite.

We consider an unmanned aerial vehicle enabled (UAV-enabled) fog-radio access network (F-RAN) in which UAVs are considered as flying remote radio heads (RRH) equipped with caching and cooperative communications capabilities. We are mainly focus on probabilistic/randomized content placement strategy, and accordingly formulate the content placement as an optimization problem. We then study the efficiency of the proposed content placement by evaluating the average system capacity and its energy-efficiency. Our results indicate that cooperative communication plays an essential role in UAV-enabled edge communications as it effectively curbs the impact of dominant Line-of-Sight (LOS) received interference. It is also seen that cooperative cache-enabled UAV F-RAN performs better in high-rise environments than dense urban and sub-urban environments. This is due to a significant reduction of the received LOS interference because of blockage by the high-rise buildings, and the performance gain of cooperative communication on the attending signal. Comparing the performances of the developed content placement strategy and conventional caching techniques shows that our proposed probabilistic/randomized caching outperforms the others in most of the practical cases.

In this paper, we propose a fast decoder algorithm for uniquely decodable (errorless) code sets for overloaded synchronous optical code-division multiple-access (O-CDMA) systems. The proposed decoder is designed in a such a way that the users can uniquely recover the information bits with a very simple decoder, which uses only a few comparisons. Compared to maximum-likelihood (ML) decoder, which has a high computational complexity for even moderate code lengths, the proposed decoder has much lower computational complexity. Simulation results in terms of bit error rate (BER) demonstrate that the performance of the proposed decoder for a given BER requires only 1 - 2 dB higher signal-to-noise ratio (SNR) than the ML decoder.

Providing seamless connection to a large number of devices is one of the biggest challenges for the Internet of Things (IoT) networks. Using a drone as an aerial base station (ABS) to provide coverage to devices or users on ground is envisaged as a promising solution for IoT networks. In this paper, we consider a communication network with an underlay ABS to provide coverage for a temporary event, such as a sporting event or a concert in a stadium. Using stochastic geometry, we propose a general analytical framework to compute the uplink and downlink coverage probabilities for both the aerial and the terrestrial cellular system. Our framework is valid for any aerial channel model for which the probabilistic functions of line-of-sight (LOS) and non-LOS links are specified. The accuracy of the analytical results is verified by Monte Carlo simulations considering two commonly adopted aerial channel models. Our results show the nontrivial impact of the different aerial channel environments (i.e., suburban, urban, dense urban, and high-rise urban) on the uplink and downlink coverage probabilities and provide design guidelines for best ABS deployment height.

Slow fading broadcast channels can model a wide range of applications in wireless networks. Due to delay requirements and the unavailability of the channel state information at the transmitter (CSIT), these channels for many applications are non-ergodic. The appropriate measure for designing signals in non-ergodic channels is the outage probability. In this paper, we provide a method to optimize space-time block codes (STBCs) based on the outage probability at moderate SNRs. Multilevel polar coded-modulation is a new class of coded-modulation techniques that benefits from low-complexity decoders and simple rate matching. In this paper, we derive the outage optimality condition for multistage decoding and propose a rule for determining component code rates. We also derive an upper bound on the outage probability of STBCs for designing the set-partitioning-based labeling. Finally, due to the optimality of the outage-minimized STBCs for long codes, we introduce a novel method for the joint optimization of short-to-moderate length polar codes and STBCs.

This paper is concerned with the energy efficiency maximization problem in multiple-input multiple-output interference channels. In the first part of this paper, we design the transmit beamforming matrices based on three approaches. In the first approach, each user tries to maximize their own energy efficiency, resulting in what we call the selfish method. The maximization problem corresponding to the selfish method can be implemented in a distributed manner with a semi-closed-form solution. The second approach aims to maximize the weighted sum-energy efficiency; we refer to it as the unselfish method. The proposed unselfish method has the following advantages. 1) It is easily implementable as the problem can be divided into several sub-problems and solved in a distributed manner. 2) It results in a quadratically constrained quadratic program with a semi-closed-form solution. Also, we consider fairness with the minimum-energy efficiency maximization problem, an approach that results in what we call the worst-case method. In the second part, general energy efficiency maximization in the presence of norm-bounded channel uncertainty is investigated which leads to a semi-definite program; the resulting method is called the robust method. Finally, the effect of the system parameters is evaluated through simulations.

In this paper, we leverage concepts from stochastic geometry to investigate the downlink performance of a vertical heterogeneous network (VHetNet) comprising aerial base stations (ABSs) and terrestrial base stations (TBSs). We model the ABSs as a 2D Poisson point process (PPP) deployed at a particular altitude while the TBSs are modelled as a 2D PPP deployed on the ground. The proposed analytical framework adopts an appropriate air-to-ground (A2G) channel model that incorporates line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions. We begin the main technical part of the analysis by deriving analytical expressions for the distribution of the distances between a typical user and the closest LoS ABS, NLoS ABS, and TBS. After that, we derive expressions for the probabilities that a typical user is associated with a NLoS ABS, LoS ABS, or TBS. Under the assumption that A2G and terrestrial channels experience Nakagami-m fading with different m parameters, we derive an expression for the Laplace transform of interference power. Furthermore, we derive exact and approximate analytical expressions for the coverage probability and achievable rate. We show that these approximations match the simulations with negligible errors for small SINR thresholds and m parameters of Nakagami-m fading.

Agile networking can reduce over-engineering, costs, and energy waste. Toward that end, it is vital to exploit all degrees of freedom of wireless networks efficiently, so that the service quality is not sacrificed. In order to reap the benefits of flexible networking, we propose a spatial network configuration (SNC) scheme, which can result in efficient networking; both from the perspective of network capacity and profitability. First, the SNC utilizes the drone-base-stations (drone-BSs) to configure access points. Drone-BSs are shifting paradigms of heterogeneous wireless networks by providing radically flexible deployment opportunities. On the other hand, their limited endurance and potential high cost increase the importance of utilizing drone-BSs efficiently. Therefore, second, user mobility is exploited via user-in-the-loop (UIL), which aims at influencing users' mobility by offering incentives. The proposed uncoordinated SNC is a computationally efficient method, yet, it may be insufficient to exploit the synergy between the drone-BSs and UIL. Hence, we propose a joint SNC, which increases the performance gain along with the computational cost. Finally, the semi-joint SNC combines the benefits of the joint SNC with computational efficiency. The numerical results show that the semi-joint SNC is two orders of magnitude faster than the joint SNC, and a profit of more than 15% can be obtained compared to conventional systems.

Recently, the utilization of aerial base stations (ABSs) has attracted a lot of attention. For the static implementation of ABSs, it has been shown that if the ABSs are statistically distributed in a given height over a cell, according to a binomial point process (BPP), a fairly uniform coverage across the cell is achievable. However, such a static deployment exhibits poor performance in terms of average fade duration (AFD) for the static or low speed moving users and power consumption. Therefore, considering a network of moving ABSs is of practical importance. On the other hand, once such a moving ABS network is considered, the coverage probability may not necessarily remain at an acceptable level. This paper is concerned with the design of stochastic trajectory processes such that if according to which the ABSs move, in addition to improving the AFD, an acceptable coverage profile can be obtained. We propose two families of such processes, namely, spiral and oval processes, and analytically demonstrate that the same coverage as the static case is achievable. We then focus on two special cases of such processes, namely, radial and ring processes, and show that the AFD is reduced about two orders of magnitude with respect to the static case. To obtain a more practical scenario, we also consider deterministic counterparts of the proposed radial and ring processes and show that similar coverage and AFD as the stochastic case can be obtained.

Since the development of the 4G LTE standards around 2010, the research communities both in academia and industry have been brainstorming to predict the use cases and scenarios of 2020s, to determine the corresponding technical requirements, and to develop the enabling technologies, protocols, and network architectures towards the next-generation (5G) wireless standardization. This exploratory phase is winding down as the 5G standards are currently being developed with a scheduled completion date of late-2019; the 5G wireless networks are expected to be deployed globally throughout 2020s. As such, it is time to reinitiate a similar brainstorming endeavour followed by the technical groundwork towards the subsequent generation (6G) wireless networks of 2030s. One reasonable starting point in this new 6G discussion is to reflect on the possible shortcomings of the 5G networks to-bedeployed. 5G promises to provide connectivity for a broad range of use-cases in a variety of vertical industries; after all, this rich set of scenarios is indeed what distinguishes 5G from the previous four generations. Many of the envisioned 5G use-cases require challenging target values for one or more of the key QoS elements, such as high rate, high reliability, low latency, and high energy efficiency; we refer to the presence of such demanding links as the super-connectivity.

In order to follow up with mission-critical applications, new features need to be carried to satisfy a reliable communication with reduced latency. With this regard, this paper proposes a low latency cooperative transmission scheme, where multiple full-duplex relays, simultaneously, assist the communication between a source node and a destination node. First, we present the communication model of the proposed transmission scheme. Then, we derive the outage probability closed-form for two cases: asynchronous transmission (where all relays have different processing delay) and synchronous transmissions (where all relays have the same processing delay). Finally, using simulations, we confirm the theoretical results and compare the proposed multi-relays transmission scheme with relay selection schemes.

This paper considers an aerial base station (aerial-BS) assisted terrestrial network where user mobility is taken into account. User movement changes the network dynamically which may result in performance loss. To avoid this loss, guarantee a minimum quality-of-service (QoS) and possibly increase the QoS, we add an aerial-BS to the network. For fair comparison between the conventional terrestrial network and the aerial-BS assisted one, we keep the total number of BSs identical in both networks. Obtaining the best performance in such networks highly depends on the optimal placement of the aerial-BS. To this end, an algorithm which can rely on general and realistic assumptions and can decide where to go based on the past experiences is required. The proposed approach for this goal is based on a discounted reward reinforcement learning which is known as Q-learning. Simulation results show this method provides an effective placement strategy which increases the QoS of wireless networks when it is needed and promises to find the optimum position of the aerial-BS in discrete environments.

Due to the battery-limited nature of mobile devices, improving energy efficiency (EE) of individual users and ensuring EE fairness among those users are one of the key design issues in uplink transmission of cellular networks. In this paper, we consider the joint optimization of discrete power and resource blocks allocations to maximize the minimum EE among users subject to individual power budget constraints. The optimization problem is combinatorial. Thus, we propose an efficient algorithm, based on semidefinite relaxation with Gaussian randomization, to solve the resultant non-convex problem in polynomial time complexity. The numerical results show how well the proposed algorithm performs against the optimal one and indicate the impact of discrete power levels on the fairness-oriented EE optimization.

Welcome to the IEEE JSAC special issue onAirborne Communication Networks. The goal of this special issue is to disseminate the contributions in the field of airborne communication networks.

Owing to the explosive growth of requirements of rapid emergency communication response and accurate observation services, airborne communication networks (ACNs) have received much attention from both industry and academia. ACNs are subject to heterogeneous networks that are engineered to utilize satellites, high-altitude platforms (HAPs), and low-altitude platforms (LAPs) to build communication access platforms. Compared to terrestrial wireless networks, ACNs are characterized by frequently changed network topologies and more vulnerable communication connections. Furthermore, ACNs have the demand for the seamless integration of heterogeneous networks such that the network quality-of-service (QoS) can be improved. Thus, designing mechanisms and protocols for ACNs poses many challenges. To solve these challenges, extensive research has been conducted. The objective of this special issue is to disseminate the contributions in the field of ACNs. To present this special issue with the necessary background and offer an overall view of this field, three key areas of ACNs are covered. Specifically, this paper covers LAP-based communication networks, HAP-based communication networks, and integrated ACNs. For each area, this paper addresses the particular issues and reviews major mechanisms. This paper also points out future research directions and challenges.

There has been a rejuvenated interest in the use of non-equally spaced (irregular) constellations after promising performance results were demonstrated compared with the conventional lattice constellations. This paper investigates the use of the irregular constellations in convolutionally coded systems. To enable the use of such irregular constellations in the presence of convolutional encoders, we derive a bit-error-rate (BER) upper bound expression for the downlink of the coded transmit maximum ratio combining systems operating in Nakagami-m fading environments. In addition, the analysis is extended to turbo trellis-coded modulation scenarios in which the convolutional encoders are used as the constituent codes. We demonstrate, via simulation, that commonly used performance analysis techniques in the literature fail to provide a valid BER bound in coded cases where quasi-regularity is not satisfied. In contrast, the technique proposed herein does not require the chosen pair of constellation and encoder to be quasi-regular. Furthermore, the system model includes a provision for multiple orthogonal transmission stages with different numbers of transmit antennas. Antenna correlation as well as distributed transmission is also supported by the model. Simulation results demonstrate the accuracy of the derived analytical results for a wide range of system scenarios.

This paper considers channel-unaware two-way relay networks in which two single-antenna nodes exchange information via multiple non-regenerative relays, each with multiple antennas. A novel self-interference cancellation scheme for distributed differential space-time signalling is developed. Despite the absence of channel-state information, this scheme enables self-interference to be completely eliminated, thereby maximizing the signal-to-interference-plus-noise-ratio of the nodes. First, we obtain a lower bound on the pairwise error probability (PEP) under residual self-interference and we show that this bound approaches a non-zero constant at high signal-to-noise ratios (SNRs), indicating a zero diversity order and an asymptotic error floor. Second, we derive a necessary and sufficient condition for the proposed scheme to eliminate self-interference perfectly. Proper operation of this scheme requires the relays to have an even number of active antennas and for relays with odd number of active antennas, such a scheme does not exist. Third, we show that, when self-interference is cancelled perfectly, the error floor vanishes and an upper bound on the PEP approaches zero at high SNRs. In this case, it is shown that the diversity gain is equal to the number of relays and is independent of the number of antennas per relay. Finally, it is shown that the coding gain increases with increasing the number of antennas per relay and converges to a constant as the number of relay antennas becomes large.

Cell switch-off (CSO) is an important approach to reducing energy consumption in cellular networks during off-peak periods. CSO addresses the research question of which cells to switch off when. Whereas online CSO, based on immediate user demands and channel states, is problematic to implement and difficult to model, off-line CSO is more practical and tractable. Furthermore, it is known that regular cell layouts generally provide the best coverage and spectral efficiency, which leads us to prefer regular static (off-line) CSO. We introduce sector-based regular CSO patterns for the first time. We organize the existing and newly introduced patterns using a systematic nomenclature; studying 26 patterns in total. We compare these patterns in terms of energy efficiency and the average number of users supported, via a combination of analysis and simulation. We also compare the performance of CSO with two benchmark algorithms. We show that the average number of users can be captured by one parameter. Moreover, we find that the distribution of the number of users is close to Gaussian, with a tractable variance. Our results demonstrate that several patterns that activate only one out of three sectors are particularly beneficial; such CSO patterns have not been studied before.

Terminal relays are expected to play a key role in facilitating the communication between base stations and low-cost power-constrained cellular Internet of Things (IoT) devices. However, these mobile relays require a mechanism by which they can autonomously assign the available resource blocks (RBs) to their assisted IoT devices in the absence of channel state information (CSI) and with minimal assignment conflicts. To address this problem, in this paper, we develop an autonomous sequence-based RB assignment scheme that dispenses with CSI. The sequences underlying the proposed scheme are designed using the Chinese remainder theorem (CRT). In particular, the CRT is used to combine the cyclic sequences generated by simple cyclic group structures into longer ones. The combining process introduces additional degrees of freedom in sequence generation, thereby enriching the set of RB assignment sequences. Simulation results show that the sequences generated by the proposed CRT-based scheme outperform those generated by currently available autonomous ones.

Backscatter communication (BackCom), which allows a backscatter node (BN) to communicate with the reader by modulating and reflecting the incident continuous wave from the reader, is considered a promising solution to power the future Internet-of-Things. In this paper, we consider a single BackCom system, where multiple BNs are served by a reader. We propose using the power-domain non-orthogonal multiple access (NOMA), i.e., multiplexing the BNs in different regions or with different backscattered power levels, to enhance the spectrum efficiency of the BackCom system. To better exploit power-domain NOMA, we propose setting the reflection coefficients for multiplexed BNs to be different. Based on this considered model, we develop the reflection coefficient selection criteria. To illustrate the enhanced system with the proposed criteria, we analyze the performance of the BackCom system in terms of the average number of bits that can be successfully decoded by the reader for the two-node pairing case and the average number of successful BNs for the general multiplexing case. Our results show that NOMA achieves the much better performance gain in the BackCom system as compared to its performance gain in the conventional system, which highlights the importance of applying NOMA to the BackCom system.

Enabling machine-type communication (MTC) over large scale cellular networks is a promising solution to handling the emerging MTC traffic. To enable a massive number of machines to connect to the base station, random access mechanisms and data aggregation have been largely studied separately in the literature. In this paper, we use stochastic geometry to investigate MTC over cellular with access class barring enhanced random access and data aggregation. We present an approximate yet accurate and tractable analytical framework for characterizing the MTC performance in terms of the machine type device (MTD) success probability, average number of successful MTDs and probability of successful preamble utilization. We validate the proposed model by comparison with simulations. Our results show that while the provision of more resources for the relaying phase benefits MTC, the provision of more preambles in the random access is not always beneficial to MTC. Thus, system parameters need to be chosen carefully to benefit the MTC traffic.

In this paper, we consider the design of multi-resolution non-coherent multiple-input multiple-output (MIMO) systems that enable Unequal Error Protection (UEP). A method for designing multi-layer non-coherent Grassmannian constellations is introduced. Specifically, the proposed method yields multi-layer constellations that are amenable to a natural set partitioning strategy. The resulting subsets from such partitioning are used to encode the more protected symbols. On the other hand, the less protected symbols are mapped to points within these subsets. Furthermore, we present two methods to establish the link between the gain of the more protected layer and the design parameters of this construction. Finally, we exploit the underlying structure to develop a sequential decoding approach. Numerical results suggest that employing such decoding scheme leads to computational savings with respect to the optimal decoding while maintaining comparable performance.

In this paper, we investigate the detection problem of binary faster-than-Nyquist (FTN) signaling and propose a novel sequence estimation technique that exploits its special structure. In particular, the proposed sequence estimation technique is based on sphere decoding (SD) and exploits the following two characteristics about the FTN detection problem: 1) the correlation between the noise samples after the receiver matched filter, and 2) the structure of the intersymbol interference (ISI) matrix. Simulation results show that the proposed SD-based sequence estimation (SDSE) achieves the optimal performance of the maximum likelihood sequence estimation (MLSE) at reduced computational complexity. This paper demonstrates that FTN signaling has the great potential of increasing the data rate and spectral efficiency substantially, when compared to Nyquist signaling, for the same bit-error-rate (BER) and signal-to-noise ratio (SNR).

In this paper, we consider the problem of admission control of wireless virtual network (VN) service requests in a multi-service network with service-specific service function chains (SFC). SFC imposes traversal constraints on flows, i.e., each flow must visit certain service-specific nodes in a specific order. We leverage the fact that all flows within a service have common traversal constraints and propose a “service-based” admission control approach. For both of the service-based and flow-based approaches, we propose novel joint optimizations of backhaul and access networks for the admission control of VN service requests. We show that our optimization formulation is convex, hence computationally efficient and tractable. We also show that the proposed method is applicable to general backhaul and access networks. The simulation results show that the service-based approach incurs no loss in optimality while greatly reducing problem complexity compared to the flow-based counterpart.

In this paper, experimental measurements of the received signal strength in a smart home test bed are presented. The measurements are used to develop a topology-independent probabilistic indoor path loss model. The proposed path loss model adds a random loss component to the log-distance path loss model to account for wall penetration, reflection, scattering, and diffraction effects. The proposed model is incorporated into the network simulator ns-3 to simulate IEEE 802.11 and IEEE 802.15.4 networks. Our results indicate that the throughput of both networks degrades as the density of the nodes increases due to increasing the connectivity of the interference links.

Drone base stations (DBSs) can enhance network coverage and area capacity by moving supply towards demand when required. This degree of freedom could be especially useful for future applications with extreme demands, such as ultra reliable and low latency communications (uRLLC). However, deployment of DBSs can face several challenges. One issue is finding the 3D placement of such BSs to satisfy dynamic requirements of the system. Second, the availability of reliable wireless backhaul links and the related resource allocation are principal issues that should be considered. Finally, association of the users with BSs becomes an involved problem due to mobility of DBSs. In this paper, we consider a macro-BS (MBS) and several DBSs that rely on the wireless links to the MBS for backhauling. Considering regular and uRLLC users, we propose an algorithm to find efficient 3D locations of DBSs in addition to the user-BS associations and wireless backhaul bandwidth allocations to maximize the sum logarithmic rate of the users. To this end, a decomposition method is employed to first find the user-BS association and bandwidth allocations. Then DBS locations are updated using a heuristic particle swarm optimization algorithm. Simulation results show the effectiveness of the proposed method and provide useful insights on the effects of traffic distributions and antenna beamwidth.

Space-time block codes (STBCs) have been designed and used to achieve the diversity and multiplexing gains in multiple antenna systems. STBCs have been typically designed based on rank and determinant criteria which can provide good performance at high signal-tonoise ratios (SNRs). Later, STBCs are designed based on mutual information to provide good performance at a specific SNR corresponding to the forward error correction (FEC) code rate. However, once the FEC code and STBC are concatenated, to achieve the best performance, STBC should be designed by considering the structure of the FEC code and the corresponding decoder in addition to the code rate. Polar codes are a new class of FEC codes that benefit from a variety of low complexity decoders and simple rate matching. Polar codes can be efficiently designed for a specific channel and STBC. Therefore, by changing the parameters of a specific STBC and optimizing the polar code for each new STBC, the best match between polar codes and STBCs can be found. Throughout this paper, we introduce a simple method for joint optimization of polar codes and STBCs and show that it can substantially improve the performance of the concatenated scheme.

As one of the multiple-input-multiple-output (MIMO) techniques that work close to capacity, Hochwald and ten Brink proposed to send forward error correction (FEC) coded two-dimensional symbols from multiple antennas in each time slot and decode them using a maximum likelihood decoder. This can be generally considered as the transmission of multidimensional symbols in each time slot and here is referred to as multi-antenna multidimensional constellations (MMCs). Polar codes are a new class of forward error correction codes that benefit from simple rate matching and low complexity decoders, and therefore, facilitate the design of efficient systems. Due to the availability of partial channel state information at the receiver in time varying fading systems, the performance of uncoded MMCs can be improved by employing MMCs designed for partially coherent systems. However, the choice of the constellation in presence of FEC codes is of importance. In this paper, we propose the concatenation of the polar codes and MMC as a high-performance scheme for time varying fading systems. We further study different methods of design of the scheme in partially coherent systems and discuss the choice of the constellation.

Unprecedented bandwidth-intensive, ultra-low latency multi-media services and applications are expected to be delivered through the fifth generation (5G) network. Thus, efficient radio resource management will continue to play an important role as the network service providers strive to provide rich media contents to their users. Therefore, in this work, we consider a cross-layer based framework for efficient allocation of network resources for the transmission of HE VC encoded video streams over 5G-aligned V2X application in mmWave environments. We adopt application and physical layer models, and formulate a multi-vehicle resource allocation problem taking into account the abstracted information of the two corresponding layers. Such a framework is compared with three other standard resource allocation schemes using computed Mean Opinion Score (MOS). Numerical results show that our proposed radio resource management scheme offers a significant improvement of viewer-perceived quality of service compared to the reference approaches.

Internet of Things (IoT) holds a great promise in providing autonomous and ubiquitous connectivity between devices in future communication systems. Due to the spectrum scarcity, very high frequency (VHF) and ultra high frequency (UHF) bands are viewed as valuable resources for IoT communications, especially to connect to distant locations that are hard to reach using higher frequencies. Existing propagation models in the VHF/UHF frequency bands are mainly for broadcasting and cellular systems with high transmit antenna heights, and hence, they are not suitable for IoT communications characterized by low antenna heights at both the transmitter and receiver. In this paper, we present new statistical path loss and delay spread models for IoT communications based on quasi-simultaneous wideband channel measurements conducted in the VHF/UHF frequency bands (from 37.8 to 370 MHz) at the city of Halifax, Canada. In particular, we present two log-distance path loss models (frequency-independent path loss exponent and frequency- dependent path loss exponent), as well as, a new statistical distribution of the delay spread.

In this paper, a novel adaptive physical-layer identity detection algorithm for LTE systems is presented. The proposed algorithm can estimate the location of the primary synchronization signal (PSS) and detect the physical-layer identity in the presence of jamming or interference. Multiple parallel adaptive filters are used to suppress the jamming signal without using any a priori information about its characteristics. Each filter is designed using the linearly constrained minimum variance (LCMV) criterion to minimize the output corresponding to any received signal that does not match the signature of the PSS. The frequency response of the adaptive filters at the PSS timing detection instant is used to suppress the interference/jamming signal during physical-layer identity estimation. Simulation results are presented to illustrate the superior performance of the proposed algorithm compared to earlier non- adaptive PSS detection algorithms.

In this paper, we consider underlay in-band device-to-device (D2D) communication in a finite cellular network region. To minimize the D2D interference generated at the base station (BS), we adopt the exclusion zone mechanism, i.e., only D2D users outside the BS exclusion zone share the same resource with the cellular uplink user. Using the stochastic geometry, we develop a general framework to analytically compute the outage probability at the center-located BS and the outage probability at an arbitrarily located D2D receiver in a disk-shaped network region. To quantify the overall D2D communication performance in the finite region, the average number of successful D2D transmissions is also derived. It shows that the D2D receiver close to the cell edge or the exclusion zone experiences lower outage probability compared to the D2D receiver not close to the edge region, which illustrates the location-dependent performance. Moreover, given the outage probability constraint at the BS, which is controlled by varying the radius of the exclusion zone, we find that there is an optimum D2D receiver sensitivity that results in the maximum average number of successful D2D transmissions. The results highlight the importance of carefully choosing system parameters to extract the benefit from the exclusion zone.

Typically, forward error correction codes are designed based on the minimization of the error rate for a given code rate. However, for applications that incorporates hybrid automatic repeat request (HARQ) protocol, the throughput is a more important performance metric than the error rate. Polar codes, a new class of error correction codes with simple rate matching and low complexity decoders, can be optimized efficiently for maximization of the throughput. In this paper, we first introduce a method to design throughput-maximizing polar codes for successive cancellation decoding (SCD). Furthermore, since the optimized codes for SCD are not optimal for SC list decoders (SCLD), we propose a rate matching algorithm to find the best rate for the SCLD decoders while using the polar codes optimized for SCD. The resulting codes provide throughput close to capacity with low decoding complexity when used with Type-I HARQ.

In this paper, we design and compare multilevel polar coding (MLPC) and bit-interleaved polar coded modulation (BIPCM) for uplink sparse code multiple access (SCMA) systems that operate over fast and block fading channels. Both successive cancellation (SC) and successive cancellation list (SCL) decoding algorithms are considered. Simulation results show that, with either decoder, BIPCM performs better than its MLPC counterpart. Also, both BIPCM and MLPC exhibit a performance advantage over LTE turbo-coded and WiMAX LDPC SCMA systems when the SCL technique is used for decoding.

To enable massive machine type communication (mMTC), data aggregation is a promising approach to reduce the congestion caused by a massive number of machine type devices (MTDs). In this paper, we consider a two-phase cellular-based mMTC network, where MTDs transmit to aggregators (i.e., aggregation phase) and the aggregated data is then relayed to base stations (i.e., relaying phase). Due to the limited resources, the aggregators not only aggregate data, but also schedule resources among MTDs. We consider two scheduling schemes: random resource scheduling (RRS) and channel-aware resource scheduling (CRS). By leveraging the stochastic geometry, we present a tractable analytical framework to investigate the signal-to-interference ratio (SIR) for each phase, thereby computing the MTD success probability, the average number of successful MTDs and probability of successful channel utilization, which are the key metrics characterizing the overall mMTC performance. Our numerical results show that, although the CRS outperforms the RRS in terms of SIR at the aggregation phase, the simpler RRS has almost the same performance as the CRS for most of the cases with regards to the overall mMTC performance. Furthermore, the provision of more resources at the aggregation phase is not always beneficial to the mMTC performance.

We propose a robust resource allocation framework to improve the physical layer security in the presence of an active eavesdropper. In the considered system, we assume that both legitimate receiver and eavesdropper are full-duplex (FD) while most works in the literature concentrate on passive eavesdroppers and half-duplex (HD) legitimate receivers. In this paper, the adversary intends to optimize its transmit and jamming signal parameters so as to minimize the secrecy data rate of the legitimate transmission. In the literature, assuming that the receiver operates in HD mode, secrecy data rate maximization problems subject to the power transmission constraint have been considered in which cooperating nodes act as jammers to confound the eavesdropper. This paper investigates an alternative solution in which we take advantage of FD capability of the receiver to send jamming signals against the eavesdroppers. The proposed self-protection scheme eliminates the need for external helpers. Moreover, we consider the channel state information uncertainty on the links between the active eavesdropper and other legitimate nodes of the network. Optimal power allocation is then obtained based on the worst-case secrecy data rate maximization, under a legitimate transmitter power constraint in the presence of the active eavesdropper. Numerical results confirm the advantage of the proposed secrecy design and in certain conditions, demonstrate substantial performance gain over the conventional approaches.

Underlay in-band device-to-device (D2D) communication can improve the spectrum efficiency of cellular networks. However, the coexistence of D2D and cellular users causes inter-cell and intra-cell interference. The former can be effectively managed through inter-cell interference coordination and, therefore, is not considered in this paper. Instead, we focus on the intra-cell interference and propose a D2D mode selection scheme to manage it inside a finite cellular network region. The potential D2D users are controlled by the base station (BS) to operate in D2D mode based on the average interference generated to the BS. Using stochastic geometry, we study the outage probability experienced at the BS and a D2D receiver, and spectrum reuse ratio, which quantifies the average fraction of successfully transmitting D2D users. The analysis shows that the outage probability at the D2D receiver varies for different locations. In addition, without impairing the performance at the BS, if the path-loss exponent on the cellular link is slightly lower than that on the D2D link, the spectrum reuse ratio can have negligible decrease, while the D2D users' average number of successful transmissions increases with increasing D2D node density. This indicates that an increasing level of D2D communication can be beneficial in future networks.

We consider the design of space-time codes for the multiple-input multiple-output multicast communication systems with two classes of receivers. The first class comprises high-resolution (HR) receivers which have access to reliable channel state information (CSI) and can perform coherent detection, and the second class comprises low-resolution (LR) receivers which do not have access to CSI and can only perform non-coherent detection. We propose a layered encoding structure in which LR information available to both classes of receivers is encoded using Grassmannian constellations, and an incremental component, which is available only to the HR receivers, is encoded in the particular bases of the transmitted Grassmannian constellation points, thereby giving rise to constellations on the Stiefel manifold. The proposed structure enables reliable coherent communication of the HR information without compromising the reliability with which the basic LR information is non-coherently communicated. To effect rate-efficient communication of the incremental, HR layer, we use optimization methods on the Stiefel manifold to develop a novel technique for designing the unitary constellations directly. This approach alleviates the restriction imposed by the traditional techniques in which unitary space-time codes are constructed from scalar constellations. As such, this approach enables better control of the distance spectrum of the developed constellations and more effective utilization of the degrees of freedom that underlie the Stiefel manifold. For the LR receivers, we use maximum likelihood detection, whereas for the HR receivers, we develop a computationally-efficient two-step sequential detector which detects the LR information prior to detecting the incremental component superimposed on it. The detectors and the layered structure with the aforementioned constellations enable full diversity and maximum degrees of freedom to be achieved on the Grassmann and Stiefel manifolds.

We consider joint optimization of user-to-base-station (BS) association, and time-frequency resource block (RB) and power allocation in heterogeneous networks (HetNets). The objective is to develop a design: 1) that maximizes the number of users accommodated in the network while satisfying their quality of service demands and 2) that minimizes usage of the resources required to meet these demands. We investigate two novel instances of HetNets with opportunistic RB-reuse. In the first instance, user-to-BS associations and power allocations can be time-shared, and the RBs can be reused during the signaling interval. For this instance, it is shown that the design problem can be approximated by a problem that yields tight convex upper and lower bounds on the objective. In contrast, the second instance represents a case in which the RBs can be reused, but the user-to-BS associations and power allocations are not time-shared, and hence, fixed throughout the signaling interval. The latter case gives rise to a combinatorial optimization problem, which we provide an approximate solution for by using a polynomial-complexity two-phase approach based on semidefinite relaxation with randomization.

In this paper, we consider a heterogeneous network composed of a full-duplex macrocell and a half- duplex femtocell. The macro-base station (BS) is equipped with L antennas where each antenna can be utilized either for downlink transmission or uplink reception. On the other hand, the femto-BS is equipped with M antennas that are used for downlink transmission. We assume that each user equipment has N antennas where L>M≥N. We investigate the benefit of using interference alignment for cross-tier and full-duplex interference management and present precoding schemes that are capable of achieving the total degrees of freedom of the network. We show that full-duplex operation of the macro-BS improves the degrees of freedom (DoF) of the system when M2N, otherwise half-duplex operation is optimal. Furthermore, the DoF of the system can be improved by 50\% via full-duplex macro-BS operation when M=N.

The LTE random access procedures proposed in 3GPP for Machine Type Communication in current cellular systems may become overwhelmed when too many machine devices attempt to upload their data. In this paper, we propose a two-hop cluster random access based on slotted ALOHA communication. In each cluster, a cluster head (CH) is selected according to the channel gains. The CH aggregates data from cluster members and then initiates the LTE random access procedure to the base station. Due to the offloading from the random access channel to the slotted ALOHA, the number of contending devices is reduced, which alleviates the collision problem and results in better performance. The simplification of access procedure can also significantly decrease the energy consumption. We define a clustering metric for machine locations and we examine the impact of the metric on the performance. The simulation results show that as machine locations become more clustered, the overall performance improves.

Polar codes, ever since their introduction, have been shown to be very effective for various wireless communication channels. This together with their relatively low implementation complexity has made polar codes an attractive coding scheme for wireless communications. On the other hand, within the realm of non-coherent wireless MIMO communication, Grassmannian signalling has been shown to approach the ergodic capacity of frequency-flat block fading channels. In this paper, a novel methodology for designing polar codes that works effectively with Grassmannian signalling and a novel set partitioning algorithm for Grassmannian constellations are proposed. We compare the error rate performance of our design with that of existing schemes and show that a gain of over 1 dB over the previously known best technique, which is based on turbo codes, is possible, at much lower decoding complexity.

Agility and resilience requirements of future cellular networks may not be fully satisfied by terrestrial base stations in cases of unexpected or temporary events. A promising solution is assisting the cellular network via low-altitude unmanned aerial vehicles equipped with base stations, i.e., drone-cells. Although drone-cells provide a quick deployment opportunity as aerial base stations, efficient placement becomes one of the key issues. In addition to mobility of the drone-cells in the vertical dimension as well as the horizontal dimension, the differences between the air-to-ground and terrestrial channels cause the placement of the drone-cells to diverge from placement of terrestrial base stations. In this paper, we first highlight the properties of the drone-cell placement problem, and formulate it as a 3-D placement problem with the objective of maximizing the revenue of the network. After some mathematical manipulations, we formulate an equivalent quadratically-constrained mixed integer non-linear optimization problem and propose a computationally efficient numerical solution for this problem. We verify our analytical derivations with numerical simulations and enrich them with discussions which could serve as guidelines for researchers, mobile network operators, and policy makers.

In this paper, we develop a geometry-inspired methodology for generating systematic and structured Grassmannian constellations with large cardinalities. In the proposed methodology we begin with a small close-to-optimal “parent” Grassmann constellation. Each point in this constellation is augmented with a number of “children” points, which are generated along a set of geodesics emanating from that point. These geodesics are chosen to ensure close-to-maximal spacing. In particular, the directions of the geodesics and the distance that each “children” point is moved are chosen to maximize the pairwise Frobenius distance between the resulting constellation points. Although finding these directions directly seems difficult, by embedding the Grassmann manifold on a sphere of larger dimension, we were able to develop structures that are not only simple to generate but that also yield constellations that, under certain conditions, satisfy the maximum distance criterion and lie within a decaying gap from a tight upper bound. Numerical results suggest that the performance of the new constellations is comparable to that of the ones generated directly and significantly better than the performance of the ones generated using the exponential map.

Energy saving in cellular networks can be achieved by implementing the cell switch-off (CSO) approach in periods of light traffic. Regular static CSO (CSO patterns) is a type of CSO where the set of active cells is predetermined such that they are located on a regular grid. It is known that regular cell layouts generally provide the best coverage and downlink SINR. Furthermore, CSO patterns assure that interfering cells are as far away as possible and help in modeling interference accurately. Existing lit- erature on CSO patterns focuses only on site-level CSO (switching off entire BSs); however, significant gains can sometimes be obtained from sector-level CSO patterns (switching off individual sectors). This paper is the first to introduce and investi- gate sector- based regular CSO patterns by providing illustrative examples. We compare the performances of different CSO patterns in terms of the number of supported users. Also, we analytically compare site- based versus sector-based CSO patterns in terms of power saving. Our results show that patterns with only one of the three sectors active (each with the same orientation) can support the most users per sector, due to a favourable interference situation.

In this paper, we compare the potential gain that can be obtained from separate-antenna full-duplex transceivers in cellular networks with that obtained from cooperative operation of half-duplex base stations. The gain is characterized in terms of the total degrees of freedom (DoF). In particular, we consider a system composed of two adjacent MIMO base stations. We consider a single time-frequency resource unit that is used by each base station to communicate with one MIMO user. For the full-duplex case, we assume that each node has a configurable transceiver that can allocate some antennas to the uplink and the remainder to the downlink. We provide an upper bound on the total DoF of the full-duplex system and derive the optimal antenna allocation at each node. We compare the derived upper bound for the full-duplex transceivers with the achievable DoF in the case of half-duplex cooperative multipoint transmission. Our results indicate that the achievable DoF in the cooperative case is always greater than or equal to the upper bound on the DoF of the full-duplex system. We further investigate the case of full-duplex cooperative multipoint transmission and show that the maximum DoF gain due to full-duplex operation cannot exceed 12.5% of the DoF of the half-duplex cooperative system.

There has been a rejuvenated interest in the use of arbitrary constellations (non-equally spaced) after promising performance results were demonstrated compared to conventional grid and circular constellations. To enable the development of such constellations, in this contribution we derive an upper bound bit-error-rate (BER) expressions for coded transmit maximum ratio combining (TMRC) systems operating in Nakagami-\textit{m} fading environments with arbitrary constellations and encoder types. Unlike most existing works on error performance analysis for coded systems, a chosen pair of constellation and encoder need not be quasi-regular. Furthermore, the system model includes a provision for multiple orthogonal transmission phases with different number of transmit antennas. Simulation results demonstrate the accuracy of the derived analytical results for a range of system scenarios.

Using drone base stations (drone-BSs) in wireless networks has started attracting attention. Drone-BSs can assist the ground BSs in both capacity and coverage enhancement. One of the important problems about integrating drone-BSs to cellular networks is the management of their placement to satisfy the dynamic system requirements. In this paper, we propose a method to find the positions of drone-BSs in an area with different user densities using a heuristic algorithm. The goal is to find the minimum number of drone-BSs and their 3D placement so that all the users are served. Our simulation results show that the proposed approach can satisfy the quality-of-service requirements of the network.

In this paper, we give qualitative and quantitative arguments for the use of perturbed triangular lattice (PTL) models for generating base station deployments with any amount of spatial regularity, which is useful notably in modeling different tiers of HetNets. We use the coefficient of variation of three spatial properties of point processes to measure the regularity of two common types of PTL: uniform on disc and Gaussian. From these measurements, we are able to derive a simple formula that allows matching and interchanging the two PTL models, within about 0.1 dB error in SIR.

Mobile communications demand for data grows almost two-fold every year and cellular capacity cannot catch up with this in every location and at every point in time. Not only will congestion situations appear more frequently and severely, but also every increase of data transmitted and every new infrastructure point rolled out in order to catch up with that demand is going to consume more total power. One approach towards greener communication is to reduce the actual demand. While technical solutions on Open Systems Interconnection (OSI) layers 1 trough 7 are already being exploited, layer 8, the user, is the original cause of the exploding demand. It is naive to assume that all data is equally important and valuable. User-in-the-loop (UIL) is a paradigm which can exactly hook in at this point, and it suggests to postpone, relocate, abandon or offload less urgent demand by immediate feedback and strong incentives like discounts and penalties in certain situations (in case of congestion). In particular, this means dynamic prices depending on time, location, and congestion (load) status, similar to smart grid for electricity utilities. Accounting and billing with dynamic prices, value-added services, and smart data pricing can be a challenge, and such accounting and billing are utterly incompatible with the flat-rate (all-you-can-consume) concept. However, cellular customers are involuntarily locked into contracts, but it is the current practice in the market, which is not in accordance with the free market spirit. Flat-rate plans are practically dead, but those still luring with this name, while having caps on the maximum allowance, sell fixed-volume packages under a smoother name instead. Ultimately, we need to re-establish usage-based-pricing as a fairer and more favourable way of charging. In this paper we motivate the dynamic pricing of UIL and the upcoming new need for usage-based tariffs. We compare a few status-quo tariff plans with our novel effective quantum (eBit) tariff, which is a usage-based pricing that maps multiple usage dimensions into only one metric, eBits. The eBits will be charged for with one constant unit price dollars/eBit to give the monthly bill. All Quality of Service (QoS)-, application-, and time-dependent dynamic pricing factors are integrated into the effective quantum, so that a user can easily understand his current consumption and the bill upto that point in time. Price differentiation per QoS class is easy with constant scaling factors. %This enables smart data pricing to be easily understood by the consumers. This principle can be extended to any other chargeable metric such as kWh of electricity and m^3 of water. This ultimately enables going greener and training awareness of all consumers.

This paper studies the optimal points in the capacity region of Gaussian multiple access channels (GMACs) with constant fading, multiple antennas, and various power constraints. The points of interest maximize general rate objectives that arise in practical communication scenarios. Achieving these points constitutes the task of jointly optimizing the time-sharing parameters, the input covariance matrices, and the order of decoding used by the successive interference cancellation receiver. To approach this problem, Carathéodory's theorem is invoked to represent time-sharing and decoding orders jointly as a finite-dimensional matrix variable. This variable enables us to use variational inequalities to extend results pertaining to problems with linear rate objectives to more general, potentially nonconvex, problems, and to obtain a necessary and sufficient condition for the optimality of the transmission parameters in a wide range of problems. Using the insights gained from this condition, we develop and analyze the convergence of an algorithm for solving, otherwise daunting, GMAC-based optimization problems.

In this paper, a forward decoding procedure is developed for the compress-and-forward (CF) relaying scheme. This procedure uses a layered framework and is based on exploiting a feature of the N -to-1 mapping inherent in the underlying Wyner-Ziv binning. It is shown that exploiting this mapping enables the relaxation of the constraint on the rate of the relay codewords representing the bin indices. For the cooperative multimessage network, the proposed procedure achieves the same rate region as the short-message noisy network coding (SNNC) scheme. However, this procedure is more advantageous for other networks including the two networks presented herein. The first network is a relay chain one with two destinations, whereas the second network is a partially cooperative multimessage one with three destinations. In both networks, side information is available to a subset of the decoding nodes, but not to the rest of the nodes, and in both cases, the network benefits from the relaxation of the rate of the CF bin indices. This relaxation results in rate regions larger than those achieved by the conventional CF and SNNC.

In this paper, we consider the problem of resource allocation for secure communications in decode-and-forward (DF) relay-assisted orthogonal frequency-division multiple access (OFDMA) networks. In our setting, users want to securely communicate to the base station (BS) with the help of a set of relay stations (RSs) in the presence of multiple eavesdroppers. We assume that all channel state information (CSI) of the legitimate links and only the channel distribution information (CDI) of the eavesdropper links are available. We formulate our problem as an optimization problem whose objective is to maximize the sum secrecy rate of the system subject to individual transmit power constraint for each user and RS. As a first work which considers limited feedback schemes for secure communications in cooperative OFDMA networks, we consider the limited-rate feedback case, where in addition to transmit power and subcarrier assignments, channel quantization should be performed and boundary regions of channels should be computed. We further consider the noisy feedback channel. We solve our problem using the dual Lagrange approach and propose an iterative algorithm whose convergence is analyzed. Using simulations, we evaluate the performance of the proposed scheme in numerous situations.

In this paper, joint designs of data routes and resource allocations are developed for generic half-duplex multicarrier wireless networks in which each subcarrier can be reused by multiple links. Two instances are considered. The first instance pertains to the general case in which each subcarrier can be time-shared by multiple links, whereas the second instance pertains to a special case in which time-sharing is not allowed and a subcarrier, once assigned to a set of links, is used by those links throughout the signalling interval. Novel frameworks are developed to optimize the joint design of data routes, subcarrier schedules, and power allocations. These design problems are nonconvex and hence difficult to solve. To circumvent this difficulty, efficient techniques based on geometric programming are developed to obtain locally optimal solutions. Numerical results show that the designs developed in both instances yield performance that is superior to that of their counterparts in which frequency-reuse is not allowed.

In this paper, we develop two approaches for designing unitarily-constrained space-time block codes, which are suitable for communicating high-resolution information in layered multiple-input multiple-output broadcast channels. Unlike existing space-time codes, which are usually synthesized from standard phase-shift keying (PSK) or quadrature amplitude modulation constellations, the space-time codes proposed herein are designed using direct optimization over the unitary group. In comparison with conventional unitary space-time block codes, including Alamouti code with PSK constellations, the space-time codes generated by the proposed approaches exhibit significantly better performance, more favorable distance spectra and more effective utilization of the degrees of freedom that underlie the unitary group.

Utilizing multiple antennas at the transmitter and receiver provides higher data rates and better reliability by exploiting spatial diversity. Space-time block codes (STBCs) is a simple approach for using multiple transmit and receive antennas that has been widely employed in standards. The STBCs introduced in the literature use independent two-dimensional constellations, while the performance of orthogonal STBCs may be improved with multidimensional constellations. These constellations are transmitted by combining multiple space-time resources to form a multidimensional signal space. In this paper, we propose a method for finding optimized multidimensional constellations for orthogonal STBCs. Optimization is performed by minimizing a novel bound on the block or symbol error rate. We show that a substantial improvement in the error probability can be achieved with these novel constellations.

This paper considers a two-way relay system with time division broadcast (TDBC) protocol. In such protocol two end-sources exchange information with each other through help of a half-duplex decode- and-forward relay. To enhance the spectral efficiency in this system, we propose a novel scheme which incorporates signal space diversity with TDBC protocol. In the proposed scheme, original data symbols are rotated by a certain angle before being transmitted, and then the end- sources and the relay cooperate for transmitting in-phase and quadrature components of two consecutive rotated symbols. Thereby, the number of transmitted symbols are doubled over three time slots, which results in an increase in spectral efficiency. In particular, for this system we first derive a closed-form expression for the end- to-end (E2E) error probability with an arbitrary constellation to acquire all the resulting non- uniform constellation cases. Then, using the derived expression, we optimize the rotation angle at the different values of the signal-to-noise ratio to further improve the E2E error probability performance. Numerical results corroborate the theoretical analysis and show that the scheme proposed herein provides not only higher spectral efficiency, but also higher reliability transmission.

This paper considers a multicarrier communication system assisted by multiple relays, one for each subcarrier. The total power emitted by the source and the total power emitted by the relays are constrained to be less than the respective power budgets. The relays are assumed to operate in the full-duplex decode-and-forward mode, and the objective is to design the codebooks of the source and the relays jointly with the power allocations that maximize the total data rate that can be reliably decoded at the destination. To approach this goal, the design problem is cast as an optimization problem, which is unfortunately nonconvex and difficult to solve. The Karush-Kuhn-Tucker (KKT) system corresponding to this problem is analyzed, and despite the nonconvexity of the problem, we were able to use the KKT system to develop an efficient technique for solving it optimally.

Wireless cellular network planning benefits from accurate and realistic, yet relatively simple and manageable, spatial traffic models. User locations in cellular networks are often modeled as homogeneous (uniform) Poisson point processes (PPPs). However, the real user distributions are seldom purely homogeneous. Network users are usually concentrated at social attractors such as residential and office buildings, shopping malls, and bus stations. Wireless spectral efficiency depends significantly on the users' spatial heterogeneity, and thus relevant spatial traffic generators and models are important. In future (5G) networks, for which device-to-device (D2D), millimeter-wave (mmWave), and small-cell deployments in Heterogeneous Networks (HetNets), are promising technologies, it will become more important to have spatial traffic models which can represent the broad possibilities from completely homogeneous cases (e.g., a deterministic lattice) to extremely heterogeneous cases (e.g., highly clustered scenarios). In this paper, we study the spatial traffic heterogeneity of outdoor users in the denser areas of the city center of Paris, France. The building shape data is freely available from the OpenStreetMaps project. We measure the heterogeneity via a second-order statistic: the Coefficient of Variation (CoV) of two spatial metrics of the resulting point process: the Voronoi cell areas and the Delaunay cell edge lengths. The expected value of the CoV of these metrics allows us to study how the heterogeneity increases with the density of users. Moreover, we find that the statistical distribution of both these metrics is close to Weibull. Our results illustrate that the topology of the buildings in the city imposes a significant degree of heterogeneity on the spatial distribution of the wireless traffic.

Long term user rate in cellular networks is the product of spectral efficiency achieved and the resources (time/frequency slots) allocated. The former is related to the received SINR, while the latter is limited by the load of the associated cell. The max-SINR cell association strategy has been used in cellular networks from GSM to LTE. This strategy maximizes the possible achieved spectral efficiency but fails to account for the load imbalance. Recently, there have been several investigations on load-aware cell association as an approach to match the traffic demand with the traffic supply, in which a user may associate to a less loaded cell, even though it does not necessarily provide the maximum SINR. In other words, a user is associated with a cell to get more share of resources at the cost of lower spectral efficiency. This paper goes beyond that by proposing a new load balancing approach that can simultaneously increase the user received SINR and the share of allocated resources. This is achieved by the user-in-the-loop (UIL) paradigm, which encourages the user to move to a new location that maximizes the utility function considering the received SINR, cell load and the probability of moving. Numerical results show that the UIL can increase the mean user rate substantially in comparison to the max-SINR or the load-aware cell association strategy, and also results in a more balanced load across the network.

In recent years, energy efficiency (“greenness”) has become an important research topic in wireless networks. Environmental awareness and the increased cost of energy stimulate the research on this subject. In cellular networks, most of the power consumption takes place at the base stations (BSs). It is worth noting that the number of BSs has been steadily increasing since the 1G networks; moreover, this increase is expected to become even steeper in the foreseeable future with the advent of the small cell concept in the envisioned 5G networks. It is also worth noting that in cellular networks the traffic demand (load) in space and time is getting increasingly heterogeneous. As a result, parts of network will likely be lightly loaded at certain times. In such situations, it only makes sense to switch off as many BSs as possible without jeopardizing the key performance indicators. Although there in an increasing volume of literature on the cell switch-off (CSO) concept, to the best of our knowledge, there is no study which considers the user terminal (UT) power consumption as a key performance indicator, while the UT power efficiency indeed constitutes one of most important performance criterion in a mobile network. In many cases, switching off BSs for downlink energy efficiency may result in an uplink energy inefficiency, due to the fact that the UTs served by the switched off BSs will need to be connected to further away BSs. In this paper, we propose a heuristic CSO algorithm to achieve energy efficiency in the whole cellular network while taking into account the power consumption of UTs. We call the proposed algorithm as the user-aware CSO algorithm.

We propose a robust resource allocation framework to provide physical layer security for a multiple input single output (MISO) communication system. In the considered system, we assume that the both legitimate receiver and eavesdropper are in full-duplex (FD) mode and compare the corresponding performance to conventional cooperative jamming frameworks where a half-duplex (HD) receiver is at hand. In the present paper, the adversary intends to optimize its transmit and jamming signal parameters so as to minimize the MISO secrecy rate between the legitimate transmitter and receivers. The proposed self-protection scheme eliminates the need for external helpers and provides system robustness. Moreover, we investigate robustness against channel state information uncertainty. Optimal power allocation is obtained based on worst-case secrecy rate maximization, under legitimate transmitter power constraint in the presence of an active eavesdropper. Numerical results are then provided to confirm the advantages of using FD receivers.

The objective of this work is to determine a close-to-optimal user-to-base station (BS) association that maximizes the number of users served by the downlink of a heterogeneous network (HetNet). Such an association must not only ensure that the number of accommodated users is maximized but also that the network resources are efficiently utilized and the users' quality of service (QoS) demands are met. In its simplest form, the optimization problem that underlies this association is combinatorial NP-hard and the difficulty of solving it is aggravated in HetNets by the disparity of transmit powers and BS computational capabilities. To find close-to- optimal user-to-BS associations, we develop a two- phase method based on semidefinite relaxation (SDR) with randomization, which is a powerful method for solving a class of combinatorial problems. Unlike the majority of other polynomial complexity techniques, SDR has a provable approximation accuracy. Numerical examples show that, in comparison with other user-to-BS association approaches, the one proposed herein enables more efficient utilization of resources and a significantly higher number of users to be accommodated.

This paper considers radio resource allocation (RRA) in the downlink of an orthogonal frequency division multiple access (OFDM)-based spectrum-sharing network. The objective of RRA is to maximize the average achievable throughput subject to the primary service interference threshold and the secondary service transmit power constraint. RRA is usually implemented based on a time window T over which system parameters are averaged and checked against resource constraints. We use short-term ( T=1 time slot) and long-term ( T ≫ 1 slots) averaging as approximations to instantaneous and average constraints, respectively. RRA is also investigated for this system with long-term interference threshold and short-term interference threshold constraints. RRA optimization is a nonconvex optimization problem in which the duality principle is adopted to obtain approximate solutions. The duality gap indicates the degree of approximation in the thus-obtained solution. We prove that the duality gap corresponding to each resource allocation asymptotically decays at least with an exponential rate of T. We further show that OFDMA is, asymptotically, the optimal subcarrier assignment. We also propose a practically implementable online power and subcarrier allocation with on-the-fly channel state information measurement. An extensive simulation study has been conducted to verify the theoretically predicted duality gap behavior and to investigate the impact of different system parameters on the secondary service performance. The developed algorithms are also validated to be robust in practical settings and converge fast to theoretical bounds, and thus practically implementable.

A recent approach in modeling and analysis of the supply and demand in heterogeneous wireless cellular networks has been the use of two independent Poisson point processes (PPPs) for the locations of base stations (BSs) and user equipment (UE). This popular approach has two major shortcomings. First, although the PPP model may be a fitting one for the BS locations, it is less adequate for the UE locations mainly due to the fact that the model is not adjustable (tunable) to represent the severity of the heterogeneity (nonuniformity) in the UE locations. Moreover, the independence assumption between the two PPPs does not capture the often-observed correlation between the UE and BS locations. This paper presents a novel heterogeneous spatial traffic modeling that allows statistical adjustment. Simple and nonparameterized, yet sufficiently accurate, measures for capturing the traffic characteristics in space are introduced. Only two statistical parameters related to the UE distribution, namely, the coefficient of variation (the normalized second moment), of an appropriately defined inter-UE distance measure, and correlation coefficient (the normalized cross moment) between UE and BS locations, are adjusted to control the degree of heterogeneity and the bias towards the BS locations, respectively. This model is used in heterogeneous wireless cellular networks (HetNets) to demonstrate the impact of heterogeneous and BS-correlated traffic on the network performance. This network is called HetHetNet since it has two types of heterogeneity: heterogeneity in the infrastructure (supply), and heterogeneity in the spatial traffic distribution (demand).

In this paper, we tackle the problem of multi-cell resource scheduling, where the objective is to maximize the weighted sum-rate through inter-cell interference coordination (ICIC). The blanking method is used to mitigate the inter-cell interference, where a resource is either used with a predetermined transmit power or not used at all, i.e., blanked. This problem is known to be strongly NP-hard, which means that it is not only hard to solve in polynomial time, but it is also hard to find an approximation algorithm with guaranteed optimality gap. In this work, we identify special scenarios where a polynomial-time algorithm can be constructed to solve this problem with theoretical guarantees. In particular, we define a dominant interference environment, in which for each user the received power from each interferer is significantly greater than the aggregate received power from all other weaker interferers. We show that the originally strongly NP-hard problem can be tightly relaxed to a linear programming problem in a dominant interference environment. Consequently, we propose a polynomial time distributed algorithm that is not only guaranteed to be tight in a dominant interference environment, but which also computes an upper bound on the optimality gap without additional computational complexity. The proposed scheme is based on the primal-decomposition method, where the problem is divided into a master-problem and multiple subproblems. We solve the master-problem iteratively using the projected-subgradient method. We also show that each subproblem has a special network flow structure. By exploiting this network structure, each subproblem is solved using the network-based optimization methods, which significantly reduces the complexity in comparison to the general-purpose convex or linear optimization methods. In comparison with baseline schemes, simulation results of the International Mobile Telecommunications-Advanced (IMT-Advanced) scenarios show that the proposed scheme achieves higher gains in aggregate throughput, cell-edge throughput, and outage probability.

Routing, subchannel scheduling, and power allocation are generally treated as separate problems in relay-enhanced OFDMA-based cellular networks. They are mostly modeled using non-linear constraints to maximize either sum rate or minimum rate. Although separation of problems simplifies modeling, it can lead to suboptimal solutions which can degrades network efficiency (i.e., low sum rate or low minimum rate). Also, models that include non-linear constraints generally belong to the NP-hard class. In this study, we jointly optimize routing, subchannel scheduling, and power allocation in relay-enhanced OFDMA-based cellular networks through a novel Linear Programming (LP) framework employing discrete power levels. Our framework is comprised of LP models for the following problems: Sum Rate Maximization (SRM), Max-Min Fairness (MMF), and Joint Sum Rate Maximization and Max-Min Fairness (JSRM3F). We investigate the trade-offs in sum rate maximization and max-min fairness in terms of achievable maximum data rates and subchannel sharing by numerical evaluations of the LP models. We show that maximum data rates obtained with discrete power allocation are near-optimal even with a few discrete power levels. We provide upper bounds for joint maximization of sum rate and minimum rate. Furthermore, the results of this study reveal that fairness has a significant impact on subchannel sharing.

Future generation (5G and beyond) cellular networks have to deal not only with an extreme traffic demand increase, but also an extreme level of heterogeneity in the distribution of that demand in both space and time. Traffic modeling in the time domain has been investigated well in the literature. In the space domain, however, there is a lack of statistical models for the heterogeneous User Equipment (UE) distribution beyond the classical Poisson Point Process (PPP) model. In this paper, we introduce a methodology for the generation and analysis of spatial traffic which allows statistical adjustments. Only two parameters, namely, Coefficient of Variation (CoV) and Correlation Coefficient, are adjusted to control the UE distribution heterogeneity and correlation with Base Stations (BSs). The methodology is applied to cellular networks to show the impact of heterogeneous network geometry on network performance.

Wireless cellular networks are undergoing severe changes due to the ever increasing demand of data rate. Additionally, the demand is more and more heterogeneous (imbalanced) in time and space. Sudden peaks in demand at a certain location have to be absorbed by the network. While operators traditionally over-provisioned their own separate network capacity in order to reduce the blocking and overload probabilities, this approach seems no longer economically viable. Instead, the idea of network virtualization (NV) emerged. One aspect of NV is that resources from all operators are pooled together. Shared and virtualized resources can be better distributed among all users compared to having separate subsets of users to separate subsets of resources. This holds especially if the demand is imbalanced among the operators, as shown in this paper. In this paper the stochastic Petri net (SPN) paradigm is used to provide with a compact model of NV resource pooling (RP). In contrast to the equivalent but tedious analysis of Markovian systems the SPN approach allows a quick numeric performance evaluation with tool support, thus olfering a strong modeling advantage. The scenarios analyzed here are networks of separate operators and resources, compared to one virtualized network. In a second step the scenario includes heterogeneity in demand, i.e., a load imbalance between the providers and results show much higher gains in this unbalance.

One approach for analyzing the high signal-to-noise ratio (SNR) capacity of non-coherent wireless communication systems is to ignore the noise component of the received signal in the computation of its differential entropy. In this paper we consider the error incurred by this approximation when the transmitter and the receiver have one antenna each, and the noise has a Gaussian distribution. For a general instance of this case, we show that the approximation error decays as 1/SNR. In addition, we consider the special instance in which the received signal corresponds to a signal transmitted over a channel with additive Gaussian noise and a Gaussian fading coefficient. For that case, we provide an explicit expression for the second order term of the Taylor series expansion of the differential entropy. To circumvent the difficulty that arises in the direct computation of that term, we invoke Schwartz's inequality to obtain an efficiently computable bound on it, and we provide examples that illustrate the utility of this bound.

The green communications paradigm has been receiving much attention in wireless networks in recent years. More specifically, in the context of cellular communications, the concept of Cell Switch Off (CSO) has been recognized as a promising approach to reduce the energy consumption. The need is expected to be pressing especially in the next decade with the increasing small cell deployment. However, the cell switch on/off decisions compounded by the resource allocation task in CSO constitute a highly challenging optimization problem due to the fact that this problem can be viewed as a generalized version of the resource allocation (scheduling) problem in the conventional cellular networks without CSO, which itself is already difficult. This paper introduces a novel framework to CSO based on multiobjective evolutionary optimization. The main contribution of this paper is that the proposed multiobjective framework takes the traffic behaviour in both space and time (known by operators) into account in the optimal cell switch on/off decision making which is entangled with the corresponding resource allocation task. The exploitation of this statistical information is done in a number of ways, including through the introduction of a weighted network capacity metric. This indicator prioritizes cells which are expected to have traffic concentration resulting in on/off decisions that achieve substantial energy savings in scenarios where traffic is highly unbalanced, without compromising the QoS. The proposed framework distinguishes itself from the CSO papers in the literature in two ways: 1) The number of cell switch on/off transitions as well as handoffs are minimized. 2) The computationally-heavy part of the algorithm is executed offline, which makes the real-time implementation feasible.

Despite the rich literature on cooperative networks, employment of different modulation levels by the source and relay terminals has not been investigated thoroughly from the physical layer perspective. In this paper, we investigate the bit error rate (BER) performance of selective relaying in a multi-relay decode-and-forward cooperative network where the source and the relays transmit using different modulation levels. Specifically, we derive a closed form expression for the end-to-end (uncoded) BER. To draw further insights on the BER performance, we also provide a simpler approximate BER expression that is accurate in the high signal-to-noise ratio regime. Finally, simulation results are presented to verify the analytical results. The derived BER expressions can be utilized in various other scenarios in which the destination selects the best signal (in terms of minimizing BER) among a set of signals which use different modulation levels. The set of signals to choose from may have already been received through orthogonal channels (selection combining), or this signal set may correspond to a set of “candidate” transmissions. The latter scenario is often referred to as selective transmission; applications of this scenario include selective relaying (the setting in this paper), fast base-station selection, and coordinated multipoint transmission and reception (CoMP).

We consider the design of space-time codes for multi-resolution multiple-input multiple-output (MIMO) broadcast communication systems. Two classes of receivers are considered: high-resolution (HR) receivers, which have access to reliable channel state information (CSI) and can perform coherent detection, and low-resolution (LR) receivers which do not have access to CSI and can only perform non-coherent detection. We propose a layered encoding structure, whereby, for the LR receivers, the transmitted codewords are chosen to be points on the Grassmann manifold whereas, for the HR receivers, incremental information is encoded in the particular bases of the transmitted codewords, thereby representing points on the Stiefel manifold. For the HR receivers, we develop a computationally-efficient two-step detector. Using this detector, we show that the proposed structure enables reliable coherent communication of the incremental HR information without compromising the reliability with which the basic LR information is non-coherently communicated. We also show that this structure enables full diversity to be achieved for both LR and HR receivers. Finally, we show that this structure achieves the maximum number of degrees of freedom for non-coherent LR channels and coherent HR channels with unitarily-constrained input signals.

As well as the demand for higher data rates goes on, the rising energy costs and environmental concerns have also led researchers to increase energy efficiency in terms of bits-per-Joule. Cooperative communications with low power nodes, switching off under-utilized cells due to low traffic load and energy efficient radio resource management are some of the energy saving methods. It is observed that the conventional energy efficiency expression, which is defined as the ratio of the data rate to the total transmission power, can limit the system throughput significantly. In this paper, we introduce a novel energy efficiency expression which provides a trade-off between sum-rate maximization and total transmission power minimization in the network. A coordinated multi-cell distributed antenna system is considered and two energy efficient radio resource management schemes are investigated; either ports can be switched on and off or their transmission power can be adjusted in order to maximize energy efficiency. It is demonstrated that the proposed expression offers significant increase in data rate while keeping total transmission power low. The proposed and the traditional energy efficiency methods asymptotically converge when tradeoff effect diminishes, i.e., transmission power minimization takes the full priority.

Constellation design is a well studied topic. For instance, it is known that, when 2-dimensional (2D) signalling schemes are used, the performance gains that the optimal constellations yield in comparison to the commonly employed square constellations are rather small. However, most of the earlier literature in this area considers rather simple communication protocols (for instance, without retransmissions), even in the absence of channel coding. With the advent of advanced communication protocols as well as signal processing techniques, there has been a rejuvenated interest in constellation design in recent years. In this paper, we consider a generic two-transmission scheme which may correspond to a relay, HARQ (hybrid automatic repeat request), or CoMP (coordinated multipoint) based transmission scenario, with a maximum likelihood receiver. The system has the flexibility of using a different 2D constellation in each transmission (however, the constellation size, i.e., the number of bits per symbol, remains the same). A generic channel coding scheme for which an encoder transfer function can be written (such as, convolutional and turbo codes) is considered for the versatile Nakagami-m fading channel. The main contribution of this paper is the derivation of an upper bound on the bit error rate (BER) which is expressed as a function of the distances between the constellation points. Using proper optimization techniques, this bound (tight for the high SNR values) which captures the impact of the distances between the constellation points can enable the design of good constellations for a given coding scheme in the above explained two-transmission setting.

This paper considers the ergodic noncoherent capacity of a multiple-input multiple-output frequency-flat block Rayleigh fading full duplex relay channel at high signal-to-noise ratios (SNRs). It is shown that, for these SNRs, restricting the input distribution to be isotropic on a compact Grassmann manifold maximizes an upper bound on the cut-set bound. Furthermore, it is shown that, from a degrees of freedom point of view, no relaying is necessary and Grassmannian signalling at the source achieves the upper bound within an SNR-independent gap. When the source-relay channel is sufficiently stronger than the source-destination and relay-destination channels, it is shown that, with the number of relay transmit antennas appropriately chosen, a Grassmannian decode-and-forward scheme, which is devised herein, achieves the ergodic noncoherent capacity of the relay channel within an approximation gap that goes to zero as the SNR goes to infinity. Closed-form expressions for the optimal number of relay transmit antennas indicate that this number decreases monotonically with the source transmit power.

In this paper, a novel approach is proposed to obtain the optimal operating point of spectrum sensing in overlay spectrum sharing systems. The objective is to maximize the secondary service achievable capacity subject to the primary service collision probability as well as the other system and service constraints. In the related literature the miss detection probability, as the main reason of collision, is often considered to model the impact of spectrum sensing on the achievable ergodic capacity of the secondary service. In this paper, however, we directly consider the collision probability constraint in finding the optimal ergodic capacity instead of considering the miss detection probability. We then propose a framework in which other opportunities which lie in the wireless channel fluctuation and power allocation are also extracted in favor of achieved capacity. In addition to the conventional One-Shot (O-S) scheme, we also propose four novel approaches to solve the optimization problem: Modified-One-Shot (M-O-S) scheme, Multi-Shot (M-S) scheme, Conservative-Modified-One-Shot (C-O-S) scheme, and Restricted-Modified-One-Shot (R-O-S) scheme. Our studies show that the proposed formulation results in a higher secondary service capacity even when compared to the cases with very low miss detection probability. In the proposed schemes in this paper, the main decision parameter is the average (over fading) received interference at the secondary service receiver due to the primary service transmission, I, which can be simply measurable in the secondary transmitter. Extensive numerical studies are conducted to investigate various system aspects. Our studies further suggest that for very low, moderate, and very high values of \(I\) , the proper schemes are C-O-S, M-S, and M-O-S, respectively.

The goal of this paper is to determine the data routes, subchannel schedules, and power allocations that maximize a weighted-sum rate of the data communicated over a generic OFDMA wireless network in which the nodes are capable of simultaneously transmitting, receiving and relaying data. Two instances are considered. In the first instance, subchannels are allowed to be time-shared by multiple links, whereas in the second instance, each subchannel is exclusively used by one of the links. Using a change of variables, the first problem is transformed into a convex form. In contrast, the second problem is not amenable to such a transformation and results in a complex mixed integer optimization problem. To develop insight into this problem, we utilize the first instance to obtain efficiently computable lower and upper bounds on the weighted-sum rate that can be achieved in the absence of time-sharing. Another lower bound is obtained by enforcing the scheduling constraints through additional power constraints and a monomial approximation technique to formulate the design problem as a geometric program. Numerical investigations show that the obtained rates are higher when time-sharing is allowed, and that the lower bounds on rates in the absence of time-sharing are relatively tight.

We examine the statistical distribution of the interference produced by a cluster of many co-channel interferers: e.g., a sensor network, or a city full of active wireless devices and access points. We consider a clustered interferer layout and analyze the interference as experienced at a given point outside (and not immediately near to) the interferer area. We model the propagation paths as experiencing power law attenuation and lognormal correlated shadowing, with pairwise correlations depending on the relative positions of the two interferers. It has already been shown in literature that adding correlation to the shadowing model can give significantly different, and more realistic, results. Our solution is mostly analytical, with only a small amount of numerical integration required. Whereas both simulation and other analytical methods become very computationally demanding as the number of interferers grows, our method's complexity is independent of the number of interferers, and its precision actually improves when their number increases.

This paper considers a joint design that incorporates the physical, medium access and network layers of a generic OFDMA-based wireless network with an ad hoc topology. The network employs channel reuse, whereby a frequency subchannel might be used simultaneously by multiple nodes. In addition to being a source and/or a destination, each node can act as a half-duplex relay to assist other nodes. The design objective is to determine the jointly optimal data routes and subchannel power allocations that maximize a weighted sum of the rates that can be reliably communicated over the network. Assuming that the signals transmitted by the nodes are Gaussian, the joint cross layer design of routing and power allocation is cast as an optimization problem. Unfortunately, this problem is non-convex, and hence difficult to solve. To circumvent this difficulty, an efficient technique based on geometric programming is developed to obtain a local solution that satisfies the Karush-Kuhn-Tucker necessary optimality conditions. Numerical results show that, despite the potential suboptimality of the obtained solution, for some network scenarios, it offers significant gains over optimal scheduling-based schemes in which a frequency band is allowed to be used by one node only at any time instant.

In this paper we study the optimality of particular points in the capacity region of Gaussian multiple access channels (GMACs) with various power constraints. The points of interest maximize general rate objectives that arise in practical communication scenarios. Achieving these points constitutes the task of jointly optimizing time-sharing parameters, input covariance matrices and the order of decoding used by the successive interference cancellation receiver. To approach this problem Carathéodory's theorem is invoked to represent time-sharing and decoding orders jointly as a finite-dimensional matrix variable. This variable enables us to use variational inequalities to extend results pertaining to problems with linear objectives to more general, potentially nonconvex, problems. In particular, it is shown that for arbitrary objectives, if the power constraints are convex, it suffices for each user to use only one covariance matrix in all its allocated time slots. On the other hand, for arbitrary power constraints, if the objective is linear no time-sharing is necessary. These results significantly reduce the design complexity and render optimal signalling over GMAC more amenable to implementation in various practical scenarios.

The downlink of a coordinated multi-cell distributed antenna system is considered. In [1], coordinated port selection was shown to achieve significant performance gains. However, in the system considered therein, the ports in each cell were constrained to transmit only to user terminals (UTs) in that cell. In this work, we consider a generalization of the problem considered in [1] by alleviating this constraint. We formulate the problem of determining the ports that maximize the minimum signal to interference plus noise ratio observed by the UTs as a binary-constrained optimization problem. Observing that it is NP-hard, we propose a semidefinite relaxation and Gaussian randomization based technique to obtain close-to-optimal solutions. Our simulation results show that the performance achieved by the proposed technique approaches that of the optimal solution. It is also shown that the proposed technique outperforms the one in [1], particularly for cell-edge UTs, albeit with an increased computational complexity.

Due to the increased energy consumption of cellular access networks, energy efficiency of the systems should be considered jointly with spectral efficiency to obtain the overall performance metrics and trade-offs. Downlink coordinated multipoint (CoMP) joint transmission aided cell switch off schemes can mitigate inter-cell interference and increase energy efficiency by using the active cells to serve the users in the switched off cell. However, the performance of this newly proposed scheme is heavily dependent on the accuracy of the selected CoMP joint transmission set. In this paper, we model the multi-point channel estimation enabled via channel state information reference symbols (CSI-RS) introduced in 3GPP release 10 systems and simulate possible scenarios that would lead to inaccurate transmission set clustering: multi-point channel estimation errors and possible CoMP system delays due to CSI transfers, node processing delays and network topology limitations. In order to mitigate the effects of channel estimation errors and system delay, we propose a framework for multi- point channel estimation in CoMP systems using a time-varying interpolation filter which tracks each multipath delay tap separately for every measured point. Possible performance gains with different filter lengths are demonstrated. Simulation results are presented to show the effectiveness of the proposed scheme. In addition, proof of concept is provided for CoMP adaptive time-varying multi-point estimation filter designs, where the UEs which are being served by higher cluster degrees need to enlarge the estimation filter memory spans only for the points which are more likely to be included in the joint transmission set.

In this paper, we consider a coordinated multi-point transmission (CoMP) scheme used in a cellular system where antenna ports are distributed throughout the cell, instead of using a single base station. Two schemes are considered; either ports can be switched on and off (Binary Power Management, BPM) or their transmission power can be adjusted (Continuous Power Management, CPM). The goal is to maximize the minimum signal to interference plus noise ratio (SINR) in the network for both schemes. The first problem is NP-hard and the second one is multi- modal. We propose to use particle swarm optimization (PSO) as a solver for both problems. It is demonstrated that the proposed PSO based algorithms can efficiently solve both problems. Furthermore, through simulations, it is shown that for the same total transmit power per port, CPM outperforms BPM.

Cellular communications now and beyond 2020 faces a number of challenges. One of the trends is the ever increasing demand for data rate due to smart mobile devices with an estimated traffic growth of almost 100% per annum. Even with new cellular generation cycles every few years the same increase rate cannot be provided on the supply side. Neither anywhere nor anytime. The gap between supply and demand of wireless capacity will shorten and the conventional over-provisioning approach will not be possible anymore, especially during busy hours. The consequences are more frequent congestion situations with broken application traffic. The quality-of-experience will suffer as user expectations are high and steamed-up by advertising. An inadequate tariff system concentrating on flat-rates is also counterproductive for stability and energy-efficiency. In this paper the temporal user-in-the-loop (UIL) control approach is assumed. This user-centric model implements demand shaping by incentives in form of a dynamic usage-based tariff which adjusts based on the level of congestion in the busy hours. This is comparable to the smart grid operation principle. The novelty in this paper is the differentiated treatment for the exemplary service classes voice, video and data, for which new quantitative user response data is utilized. The control approach performance is calculated and results for stationary and dynamic scenarios are presented.

In this study, we propose a pricing based algorithm that assigns user terminals (UTs) to base stations (BSs) and optimizes the transmission powers in a way that minimizes the energy expenditure. The algorithm takes into account the fixed energy expenditure that occurs even if a BS does not transmit to any UT, therefore the proposed algorithm switches off the unnecessary BSs in order to minimize the total energy expenditure. We compare this algorithm with two benchmarks. One of them is a simple algorithm that connects each UT to the BS with best channel conditions. Transmission powers are then optimized iteratively. The second benchmark is the optimal solution that is found using a branch and bound technique. The numerical evaluations reveal that the pricing based algorithm performs very close to the optimal.

We consider a three-node quasi-static communication system with a full-duplex relay. The goal is to determine the relaying mode that enables rate-efficient communication under given channel conditions. To achieve this goal, we consider a generalized scheme that subsumes the decode-and-forward (DF) and compress-and-forward (CF) schemes as special cases. The generalized scheme is considered when the source and relay signals are synthesized from commonly-used Gaussian codebooks, which are shown to be capacity achieving in two asymptotic cases: perfect relay-destination link and broken source-destination link. Studying the generalized DF-CF scheme, it is shown that, for two non-asymptotic cases in which the signal-to-noise ratios (SNRs) of the links satisfy certain conditions, this scheme reduces to either DF or CF. For another set of non-asymptotic SNRs, the generalized scheme is shown to yield strictly higher rates than both DF and CF. Despite the complexity of the generalized scheme, its rate advantage over DF and CF is shown to be upper bounded by 0.5 bits per channel use. This indicates that the practical benefit of the analysis of this scheme is to enable selecting the relaying mode that suits a given channel realization. Numerical results show that, under Rayleigh fading conditions, this selection yields significant gains over fixed DF and CF.

The maximum data rate that can be achieved by the strictly causal full-duplex amplify-and-forward (AF) scheme in general Gaussian relay channels is achieved by Gaussian codebooks and can be cast as the solution of an optimization problem of the input transmit covariance and relay precoder. This problem possesses an intricate nonconvex structure and is hence difficult to solve. To circumvent this difficulty, the relay precoder is assumed to be given and then the Karush-Kuhn-Tucker conditions are used to obtain closed form expressions for the optimal input covariance corresponding to that precoder. These expressions are used to show that subdiagonal precoders suffice to attain the maximum achievable rate of the AF scheme at any source transmit power. In addition to significantly reducing the effort expended in searching for the optimal relay precoder, this observation enables us to find the optimal precoders at low and high source transmit powers. For asymptotically low transmit powers, the optimal relaying mechanism is shown to possess an interlacing structure, thereby resembling half-duplex operation. In contrast, for asymptotically high transmit powers, it is optimal for the relay to be silent. The asymptotic analysis enables us to develop an explicit formulation for a suboptimal precoder that, at intermediate source transmit powers, are shown numerically to outperform asymptotically optimal precoders.

In this paper, we utilize stochastic geometry to analyze the primary service (PS) outage performance for spectrum sharing in Rayleigh fading environment. Using this approach, the impacts of the secondary service (SS) parameters and wireless environment on the PS outage probability are analyzed. We further obtain a closed form for the PS outage probability. The maximum SS transmitter node density for a given outage probability constraint of the PS is then obtained. We also investigate the impact of secondary spectrum sensing on the PS outage probability. A novel approach is further proposed that provides tight approximation for the PS outage probability. The results of the proposed approach are then validated through analysis and simulations. We then consider power control in the secondary network and show that the truncated channel inversion power control significantly decreases the PS outage probability. Cases with centralized and decentralized cooperative spectrum sensing are also studied, and their corresponding PS outage probabilities are analyzed. Mean spatial throughput of the SS is also analyzed. We further investigate the impact of the PS outage constraint on the spatial throughput of the SS. Extensive simulations confirm our analytical derivations.

In this paper, we propose an interference management method for underlay spectrum sharing and evaluate its performance. In this method the secondary service has been facilitated by granting passive access to the power control signalling transmitted by the primary network base station. To exploit both slow shadowing and fast fading, the proposed method performs secondary service power management in two phases, each in different time-scales: rate optimal power allocation phase and interference reduction power adjustment phase. In the longer time-scale, rate optimal power allocation phase adaptively allocates the secondary transmit power exploiting the medium-scale channel variations (shadowing effect) of the secondary channel to maximize its capacity. In the shorter time-scale, interference reduction power adjustment phase exploits the power control commands transmitted in the primary network to adaptively adjust secondary service transmission power for reducing the effects of the secondary service transmission on the Quality-of-Service (QoS) of the primary service network. The main advantage of this method which is referred to as Adaptive Multiple Time-Scale Power Allocation (AMTPA) is that it does not require direct signaling between the two systems. We further present AMTPA analytical performance evaluation results. Practical considerations are also presented regarding the primary network requirements and its power control feasibility after adopting AMTPA in the secondary network. Extensive simulation results indicate significant improvement in the system performance by using AMTPA. Using simulations we also show how one can set AMTPA parameters so that a certain level of QoS in the primary network is satisfied.

The focus of this paper is on studying the tradeoff between the sum efficiency and Jain's fairness index in general resource allocation problems. Such problems are frequently encountered in wireless communication systems with M users. Among the commonly-used methods to approach these problems is the one based on the α-fair policy. Analyzing this policy, it is shown that it does not necessarily achieve the optimal Efficiency-Jain tradeoff (EJT) except for the case of M=2 users. When the number of users M>2, it is shown that the gap between the efficiency achieved by the α-fair policy and that achieved by the optimal EJT policy for the same Jain's index can be unbounded. Finding the optimal EJT corresponds to solving a family of potentially difficult non-convex optimization problems. To alleviate this difficulty, we derive sufficient conditions which are shown to be sharp and naturally satisfied in various radio resource allocation problems. These conditions provide us with a means for identifying cases in which finding the optimal EJT and the rate vectors that achieve it can be reformulated as convex optimization problems. The new formulations are used to devise computationally-efficient resource schedulers that enable the optimal EJT to be achieved for both quasi-static and ergodic time-varying communication scenarios. Analytical findings are confirmed by numerical examples.

In this paper, we investigate practical turbo receiver design for throughput-efficient relay ARQ transmissions over broadband cooperative MIMO channels. Our setup is comprised of three multi-antenna nodes: a source, a destination, and a relay node operating under the amplify-and-forward half-duplex relaying mode. To attain higher system average throughput, we adopt a two-slot transmission strategy where the ARQ mechanism is activated on top of the amplify-and-forward protocol. We derive a soft sub-packet combiner allowing the destination node to jointly perform sub-packet combining (over both time-slots and multiple ARQ rounds) and frequency domain (FD) MMSE filtering. Its computational load is then smoothly relaxed via a recursive implementation alleviating the memory requirements when the number of ARQ rounds increases. Simulation results show that the proposed transmission strategy along with turbo sub-packet combining at the destination side achieves significant average throughput performance gain compared with conventional ARQ-based cooperative relaying, especially in situations where the relay node is close to the source node.

We consider the downlink of a cellular system in which each base station (BS) has multiple distributed antenna ports that are geographically dispersed over the cell. The goal of the BSs is to improve cell-edge performance by selecting the subset of ports that maximizes the minimum signal-to-interference-plus-noise ratio of the user terminals in a coordinated manner. This problem is cast as a binary-constrained optimization problem, which is known to be NP-hard. To circumvent this difficulty, the semidefinite relaxation technique is used to efficiently generate Gaussian distributed vectors. Simulation results show that rounding a relatively small number of these vectors yields close-to-optimal solutions of the original problem.

The cellular network users are demanding more traffic and expecting a ubiquitous high data rate. Several techniques are investigated to cope with this massive demand. All the current solutions are focusing on improving this issue from the supply side. However it is expected that a significant improvement on the system performance can be gained if the end users become a part of the system and not just consumers. The user-in-the-loop (UIL) spatial approach provides a solution to fulfil the increasing traffic demand by convincing users to move to locations with higher SINR values. The previous results of the UIL are promising and a higher cell spectral efficiency is achieved only by the involvement of the users and without any investment on the supply side. UIL is a user-centric approach that offers an incentive to influence users' behavior to participate in the system improvement process. In this paper, the previous ideal environment of UIL is extended by integrating the important effect of correlated shadowing to the analysis of UIL performance. The obtained results in this paper confirm the previous results and promise significant performance improvements. Results show that the achieved higher spectral efficiency and the required moving distance depend on the correlation parameters.

With the ever increasing usage of wireless devices, the power consumption and energy efficiency of cellular networks have become important performance indicators. In recent years, various types of energy saving schemes have been proposed for cellular networks. However, most of these schemes do not take advantage of the advanced features offered by the recent cellular standards, such as the 3GPP release 10 (also known as LTE-Advanced) and beyond. One of the recently proposed energy saving schemes in cellular networks is the cell switch off technique in which a lightly loaded cell is completely switched off and the traffic in that region is absorbed by the nearby cells whose transmit powers are increased to enable larger coverage areas. In this paper, we propose to use the cell switch off scheme without increasing the transmit powers of the active cells; instead, we propose to use the coordinated multipoint (CoMP) transmission radio technology, which is being standardized for the beyond-LTE cellular networks, to enable a sufficient received power level from a number of nearby cells whenever possible. Through simulations with realistic parameters, we demonstrate that the above described cell switch off + CoMP combination used with proper CoMP active set degree yields a more energy efficient solution with better received user experience in comparison to the traditional cell switch off schemes. In the proposed scheme, channel estimations are needed in order to determine the CoMP active set accurately. We analyze the performance by modeling the channel estimation error as a Gaussian random variable with 4, 8, and 12 dB standard deviation and observe that perfect channel estimation can provide up to 46% capacity and 24% energy efficiency increase in comparison to the scenarios with the channel estimation errors.

In this paper an OFDMA-based wireless ad hoc network is considered. In addition to the potential of being a source and/or a destination, each node is assumed to be capable of decoding and forwarding its received packets to other nodes in the network. The goal is to determine the optimal data routes, subchannel schedules, and power allocations that maximize a weighted sum rate of the data communicated over the network. Two instances of this problem are considered. In the first instance, each subchannel is exclusively used on one of the links, whereas in the second instance subchannels are allowed to be time shared by multiple links. The first problem gives rise to an NP-hard mixed integer optimization problem that is difficult to solve. In contrast, using a change of variables, the second problem is cast in a convex form, which is amenable to highly efficient interior point solvers. Simulation results suggest that the gain in the weighted sum rate achieved by the relaxation in the second problem over that achieved by the original mixed integer problem is negligible for small networks, and increases with the size of the network.

The capacity of the amplify-and-forward (AF) scheme in general full-duplex Gaussian relay channels is achieved by Gaussian codebooks and can be cast as the solution of an optimization problem of the input transmit covariance and the relay precoder. This problem is non-convex. To circumvent this difficulty, the Karush-Kuhn-Tucker (KKT) conditions are used to obtain closed form expressions of the optimal input covariance that corresponds to an arbitrary relay precoder. Using these expressions, it is shown the maximum rate of the AF scheme is achieved by subdiagonal precoders. This observation is used to facilitate the search for the optimal relay precoder, and to show that at high transmit powers, it is optimal for the relay to remain silent and, at low transmit powers, it is optimal to operate in a mode that resembles half-duplex operation.

This paper considers the ergodic non-coherent capacity of a multiple-input multiple-output frequency-flat block Rayleigh fading relay channel. It is shown that for this channel restricting the input distribution to be isotropic on a compact Grassmann manifold maximizes an upper bound on the cut-set bound at high signal-to-noise ratios (SNRs). Furthermore, Grassmannian signalling at the source achieves this bound within an SNR-independent gap. For moderate-to-high SNRs, a Grassmannian decode-and-forward (DF) relaying scheme is devised, and the optimal signalling dimensionality that minimizes the gap to the upper bound is obtained.

Increasing cellular traffic is the driving force for innovations in wireless communications. While voice traffic is not expected to increase much and does not require 4G systems, traffic for video and data applications is expected to grow with a rate of 100% per year. Smart mobile devices, tablets and laptop dongles will certainly make this a reality. On the other hand the supply side cannot grow with the same rate. Base stations, eNB, pico- and femtocells will bring more heterogeneity in space and new applications will bring more heterogeneity in demand over time. Designing for over-provisioning capacity has been the standard approach to stabilize traffic, but is will be harder and harder, with more congestion situations in time (busy hour) and space (crowded cell) which will break application traffic and give bad quality-of-experience of users. Furthermore, over-provisioning comes with more power consumption and higher financial expenditures for infrastructure and operating costs. The user-in-the-loop (UIL) approach offers a solution orthogonal to the traditional supply-only view. In addition to technical improvements, having a temporal demand control can alleviate the severity of busy-hour situations which formerly caused congestion and connection failures. Demand shaping is implemented by a dynamic usage-based tariff and adaptive rates depending on the load condition. The users in a cell are part of a closed control loop which reacts in cases of severe demand overload. In this paper three different service classes are controlled individually and results from analysis and simulation show the performance in stationary and dynamic scenarios. The economics of tariffs and dynamic prices and the resulting operator revenue on one side is compared to the dissatisfaction of rejected users and this gives decision indicators for the investment into new infrastructure. Overall this saves money, energy and turns situations of hard congestion into an elastic stationarity which is in the interest of both users and operators.

In this paper, we study tradeoff policies between efficiency and the Jain's fairness index of the benefits received by M users in general resource allocation scenarios. Analyzing the commonly-used α-fair tradeoff policy, it is shown that, except for the case of M =2 users, this policy does not necessarily achieve the optimal Efficiency-Jain tradeoff. In particular, it is shown that, when the number of users M >;2, the gap between the efficiency achieved by the α-fair and the optimal Efficiency-Jain tradeoff policy can be unbounded, for the same Jain's index. Finding the optimal Efficiency-Jain tradeoff for arbitrary set of admissible benefits is generally difficult. To alleviate this difficulty, we derive sufficient conditions, which, when satisfied by the set of admissible benefits, lead to efficiently computable optimal tradeoff and benefit vectors. Numerical results for a typical communication network scenario are provided to confirm analytical findings.

Traffic in mobile radio networks is expected to continue to increase by 100% per year. This imposes a big challenge for future generations (4G and 5G) of access technologies which were previously dimensioned for over-provisioning especially in the busy hours. Recent forecasts hint that this assumption and approach will not be tractable anymore. Instead the demand will exceed the supply more and more frequently causing congestion not only in short term but over longer periods of time. Any approach to engineer networks greener by better spectral efficiency only will fail to meet the global objective, if demand keeps on increasing faster than supply and efficiency. Instead, the User-in-the-loop approach allows to shape the demand where it originates either in space or time. This is achieved by incentives for users to change location or by dynamic tariffs to shift the use of congested resources out of the busy hours. This works as the smart grid of communications. While some work has already been done in this new field, the user behavior to the controlled demand shaping was based on assumptions. In this paper, the assumptions were confirmed by recent survey results which indicate that shaping user behavior works sufficiently well. Models of the user response are given and analysis and simulation results show the advantages and gains of this approach.

Resource scheduling in OFDMA cellular wireless networks is a powerful technique on the MAC layer. Utilizing adaptive modulation and coding allows the effective use of all signal-to-interference ratio (SINR) ranges. Typical single antenna spectral efficiency values for LTE-Advanced range between 4.8 near the base station and 0.2 b/s/Hz at the cell edge. With best-effort traffic and full buffer assumption the tradeoff between emphasizing the cell center or cell edge can be explored extensively. However, with real-time traffic present, this takes priority without fairness adjustment alternatives. In this paper the mixed traffic scenario is studied with an abstract stochastic Petri net model. The exploration of the degrees of freedom by studying the real-time traffic proportion and a fairness adjustment parameter provides new insight to the potential feasible region. The results show that the tradeoff between emphasizing the cell edge performance and maintaining a high average spectral efficiency is most powerful in the best-effort case, while an increasing level of real-time traffic reduces the room for a tradeoff. The stochastic Petri net analysis approach allows numeric analysis without simulation by utilizing Markov chain equivalence and steady state calculations. This model is deliberately abstract but flexible enough to study the tradeoff.

In this paper, we consider coordinated downlink transmission in a cellular system wherein each base station (BS) has multiple geographically dispersed antenna ports. Each port uses a fixed transmit power and the goal of the BSs is to collectively determine the subset of ports and the corresponding beam steering coefficients that maximize the minimum signal-to-interference-plus-noise ratio observed by the user terminals. This problem is NP-hard. To circumvent this difficulty, a two-stage polynomial-complexity technique that relies on semidefinite relaxation and Gaussian randomization is developed. It is shown that, for the considered scenarios, the port state vectors and beam steering coefficients generated by the proposed technique yield a performance comparable to that yielded by exhaustive search, but with a significantly less computational complexity. It is also shown that the proposed technique results in significant power savings when compared with other transmission strategies proposed in the literature.

Terminal relaying offers an effective means for improving the performance of OFDMA-based wireless networks. However, \revrr{with the increase in the number of relaying terminals (RTs), their coordination becomes a cumbersome task.} To address this drawback, in this paper an autonomous scheme is proposed whereby the RTs assign resource blocks (RBs) to incoming wireless terminals (WTs) in a way that minimizes the number of hit occurrences at which the same RB is assigned to multiple WTs. The proposed scheme uses cyclic group generators to determine the sequence of RBs to be assigned by each RT. This scheme is particularly beneficial in terminal relaying systems in which the distribution of the WTs is nonuniform and the channel quality indicators are not available. Simulation results show that the proposed scheme performs significantly better than currently available autonomous assignment schemes.

In this paper, the generalization of the decode-and-forward (DF) and compress-and-forward (CF) relaying schemes is studied for the case in which Gaussian codebooks are used for signalling over scalar Gaussian memoryless channels. Three SNR regions are identified wherein the generalized DF-CF scheme reduces to either the DF or the CF scheme. In addition, it is shown that there is an SNR region in which the generalized DF-CF scheme can be more advantageous than both schemes.

We analyze the uplink outage probability at the randomly, but not necessarily independently nor homogeneously, distributed cellular network receivers caused by an overlaid secondary ad hoc network. Unlike the previous works that assumed exact circular exclusion regions (or guard zones), we incorporate a sensing mechanism to decrease the significant effect of the nearby interferers. We consider Rayleigh-faded links, large-scale power control, and the knowledge of the initial outages in the absence of the secondary network. We derive an upper bound on the outage probability and show it to be tight when the co-channel primary receivers are separated by relatively significant distances, which is inherently the case for the cellular network. Furthermore, we derive a closed-form expression that shows the significant impact of the sensing mechanism on the spectrum sharing gains.. Additionally, we optimize the decision threshold, which is used by secondary network users to decide whether to transmit or not, in order to maximize the spectrum sharing gains under the given outage constraints.

A closed-form expression for the outage probability at a receiver in the primary network due to a uniform annular sector distribution of secondary transmitters is derived in a Rayleigh fading environment. This may include an exclusion region, and a finite or infinite outer radius. Conversely, under a given constraint on the outage probability, spectrum sharing opportunities are analyzed with respect to the field size and are shown to be significantly dependent on the exclusion region. Conditions are obtained for which an infinite field size assumption can or cannot lead to the loss of spectrum sharing opportunity. Additionally, the spectrum sharing gains (the maximum average number of secondary transmitters) are derived for the secondary network for different spatial deployments. It is worth mentioning that other than networks sharing spectrum, the derived results are applicable to any set of interferers from the same network or other networks around any receiver of study.

Cellular wireless networks based on OFDMA utilize adaptive modulation and coding to operate effectively in regions of high and low SINR. Therefore the local single antenna spectral efficiency ranges between 5 in the cell center and 0.2 b/s/Hz at the cell edge. The scheduling goal of high average spectral efficiency contradicts the goal of a good rate fairness among all terminals. Also there is a tradeoff between increasing the cell edge performance and maintaining a high average spectral efficiency. In this paper a stochastic Petri net analysis approach is taken and a numeric analysis is performed based on Markov chain equivalence and steady state calculations. The proposed models are deliberately abstract but offer commonly used tuning parameters in order to study the tradeoff without too many degrees of freedom.

In this paper, we propose a novel distributed inter-cell interference coordination (ICIC) scheme. The proposed scheme, which runs in polynomial time, finds a near-optimum dynamic resource partitioning that maximizes a proportional-fairness criterion in the entire network. The proposed scheme is based on primal-decomposition method, where the problem is divided into a master and multiple sub-problems. The master-problem is solved using projected-subgradient method while each of the sub-problems is solved using minimum-cost network flow optimization. Through extensive simulations of four IMT-advanced scenarios, we quantify the gains achieved using the proposed scheme. We demonstrate that the proposed scheme achieves both high cell-edge throughput, that is comparable to frequency reuse 3, and high aggregate throughput, that is at least as good as the aggregate throughput achieved by frequency reuse 1.

A cellular distributed antenna system (DAS) allows mobile user terminals (UTs) to be served at higher data rates as compared to conventional cellular systems by reducing the path loss and attaining macrodiversity gains. Moreover, such a cellular DAS can be perceived as a distributed multi-user multiple-input multiple-output system. In this paper, the block diagonalization and zero-forcing dirty-paper coding downlink transmission schemes are extended for a single-cell DAS with multi-antenna distributed antenna ports (DAPs) and multi-antenna UTs, where only a subset of all DAPs in the cell transmit to each UT. The aggregate cell spectral efficiency that is achieved by these schemes per frequency-time resource block is compared for both DAS and collocated antenna system (CAS) architectures, subject to the same total power constraint. The gains of the cellular DAS over the cellular CAS are demonstrated, and the effect of the number of antennas per DAP on the performance of the cellular DAS is investigated.

Error performance is one of the main performance measures and the derivation of its closed-form expression has proved to be quite involved for certain systems. In this paper, a unified closed-form expression, applicable to different binary modulation schemes, for the bit error rate of dual-branch selection diversity based systems undergoing independent but not necessarily identically distributed generalized-K fading is derived in terms of the extended generalized bivariate Meijer G-function.

Effective coordination between terminal relays in OFDMA-based systems becomes a cumbersome task with the increase in the number of these relays. To address this drawback, in this paper an autonomous scheme is proposed whereby the relays assign resource blocks to incoming wireless terminals (WTs) in a way that minimizes the number of hit occurrences at which the same resource block is assigned to other WTs. The proposed scheme uses cyclic group generators to determine the sequence of resource blocks to be assigned by each relay. This scheme is particularly beneficial in self-organizing terminal relaying systems in which the distribution of the wireless terminals is nonuniform. Simulation results show that the performance of the proposed scheme is significantly better than that of assignment schemes proposed in the literature.

In this paper the system analysis of modern wireless systems is simplified by providing simple yet powerful models for the wireless channel in the environment of higher layer abstract system descriptions with generalized stochastic Petri nets (SPN). This modeling approach is capable of deriving performance metrics in terms of packet delays even under heterogeneous, asymmetric, bursty and underutilized traffic conditions, because they are easy to model with SPN. The missing link in wireless systems are suitable channel models, which can now be used as a plug-in submodel inside a larger composite Petri net model. A number of models are proposed, starting from the finite-state Markov channel model approach. Performance results for a multihop relayed transmission under varied traffic load show the utility of this modeling approach.

This paper studies QoS-guaranteed fair resource allocation and packet scheduling for OFDMA networks. We start with non-traffic-aware weighted generalized proportional fair (WGPF) scheduler and make it a delay-fair scheduler. Delay-fair objective is to equalize delay dissatisfaction measures among users. In this paper the focus is on mean queuing delay. We show how the WGPF objectives are connected and are equivalent to delay-fair scheduler, derived from Little's law. We see that our fair framework can also be interpreted as minimizing the total delay dissatisfaction that users are experiencing. This framework extends the conventional fairness notions in order to handle heterogeneity of traffic in time and among users which is an important emerging problem. We then study an important special case, which is min-max delay fairness. We prove that the developed framework for a special dissatisfaction function, asymptotically leads to the min-max average delays. The developed framework, in this special case, can be adjusted between two extreme objectives: minimizing total average delay among users and minimizing the maximum average delay among users. Finally we prove that the gradient scheduling algorithm is equivalent asymptotically to serving the user with the largest average delay at each iteration.

In this letter, the effect of the input power constraint on the beamforming optimality range in Gaussian two-input multiple-output (TIMO) channels is investigated. The obtained expressions, using standard Lagrangian formulation, determine explicitly the range of the input signal-to-noise ratio (SNR) for which beamforming (rank-1 signaling) is optimal for TIMO channels for both the common power constraint and the individual power constraint cases. Moreover, the obtained results are extended to random TIMO channels, with channel state information at the receiver only, using the Jensen's upper bound on the mutual information.

Error performance is one of the main performance measures and the derivation of its closed-form expression has proved to be quite involved for certain communication systems operating over composite fading channels. In this letter, a unified closed-form expression, applicable to different binary modulation schemes, for the bit error rate of dual-branch selection diversity based systems undergoing independent but not necessarily identically distributed generalized-K fading is derived in terms of the extended generalized bivariate Meijer G-function.

This paper proposes a signal-level turbo packet combining strategy for multi-antenna multi-rate relay-assisted systems operating over broadband channel. We derive a fixed rate equivalent multi-antenna system communication model where the multi-rate multi-node received signals can be viewed as direct retransmissions from a virtual node with a fixed transmission rate. Using virtual antenna concept, we introduce a frequency domain turbo packet combiner inspired by minimum mean square (MMSE) criterion. Block error rate (BLER) performance are provided to demonstrate the gains offered by the proposed combining scheme over the conventional soft information-based combining.

We devise an optimization framework for generalized proportional fairness (GPF) under different time scales for amplify-and-forward (AF) relay networks. In GPF scheduling, a single input parameter is used to change the fairness from throughput optimal, to proportionally fair and asymptotically to max-min fair. We extend the GPF scheduling to include a new input parameter, which determines the time-scale of fairness from short-term GPF to long-term GPF. We devise a low-complexity near-optimal algorithm to find schedules satisfying the given fairness criteria in a given time-scale. Simulations show that the proposed algorithm indeed allows the flexibility to change the fairness and its time- scale. To the best of our knowledge, this paper is the first to provide a multi-user scheduling framework for AF relays with both flexible fairness and flexible time-scales.

Appropriate channel modeling plays an important role in the design and analysis of various transmission and reception schemes over composite fading channels. The generalized-K (Gamma-Gamma) composite fading model has been used recently to model composite fading in wireless channels as an alternative to the less tractable lognormal-based models. In this paper, the expression of the amount of fading for the sum of correlated generalized-K random variables is derived and then the moment matching method is used to approximate, in the lower tail region, the probability density function of the sum of identically distributed generalized-K random variables with positively and equally correlated shadowing components by the familiar Gamma distribution. Furthermore, the obtained expressions of the amount of fading give insights into the effect of shadowing correlations on the performance of maximal ratio combining receivers in coordinated multi-point transmission and reception schemes in future wireless systems.

In this paper, we utilize stochastic geometry to analyze the primary service outage performance for spectrum sharing in Rayleigh fading environment. Using this approach, the impacts of the secondary service parameters as well as the wireless environment on the primary service outage probability are analyzed. We further obtain a closed form for the primary service outage probability based on the transmit power of the secondary service as well as miss detection probability of the spectrum sensing. Furthermore, we obtain the maximum secondary service transmitter node density for a given outage probability constraint of the primary service. We also derived an upper bound to the achievable capacity of the primary service. Simulations results confirm the analytical derivations.

This paper focuses on turbo packet combining techniques for multi-relay-assisted systems operating over multiple-input multiple-output (MIMO) broadband channel. Two packet combining techniques are studied, signal-level packet combining and soft information-based combining. In this paper, a comparative study, in term of implementation cost and performance evaluation, is presented. Using complexity analysis and throughput simulations, we demonstrate that the choice of the best combining technique depends on the system configuration.

Distributed antenna systems (DASs) are expected to play an important role in the delivery of high data rates in future cellular networks. In DASs, the multipath fading and shadowing effects have to be analyzed concurrently due to the geographical scattering of the distributed antenna ports. The Gamma-Gamma (generalized-K) probability density function (PDF) has been adopted recently to model composite fading in wireless channels. In this paper, the ergodic capacity and the information outage probability for DASs in generalized-K fading channels are considered. The analysis is based on the approximation of the PDF of the weighted sum of independent generalized-K random variables by another generalized-K PDF. The obtained results provide insights into the gains of DASs in generalized-K composite fading channels.

An important parameter in determining a spectrum sharing opportunity is the level of interference power that secondary users may generate towards primary users. It is indicated in literature that the aggregate interference power of an infinite network (such as a very large secondary network) is bounded under certain conditions. However, to the best of our knowledge, no work has been devoted to determining the boundary of the dominantly interfering region. In this paper, we identify the smallest portion (dominant region) of the secondary network that would impact spectrum sharing opportunities. Our results show that the dominant region is not necessarily a small region encompassing a few interferers within the proximity of the primary user. Far interferers may tangibly contribute to spectrum sharing decisions when a higher approximation accuracy is required or when a wide exclusion region (within which no secondary users are allowed to transmit) is considered. On the other hand, the dominant region shrinks with the increase in the path-loss exponent or in the level of the interference threshold specified by the primary user or a regulator. Some implications of these results are highlighted. Moreover, the results are anticipated to inspire new ideas for designing MAC protocols for secondary networks.

This paper investigates throughput-efficient relay ARQ protocols for single carrier MIMO systems with amplify-and-forward relaying. We focus on reducing the multiplexing loss due to the half-duplex operation at the relay. We introduce two new relaying protocols where both ARQ and relaying are jointly targeted. Compared to the conventional relaying techniques, the proposed mechanisms require only one time slot duration for transmitting the entire data packet at each ARQ round. We also focus on performance evaluation, and show that the new techniques provide significant improvement in average throughput while maintaining good outage probability performance.

The importance of characterizing the aggregate interference power generated by a wireless network has increased with the emergence of different types of wireless networks such as ad-hoc networks, sensor networks, and cognitive radios. A cumulant-based characterization of this aggregate interference is an attractive approach. A number or recent papers in literature have dealt with cumulants of the aggregate interference but under specific scenarios. In this paper, we introduce a simple yet comprehensive method to determine the cumulants of the aggregate interference power originating from a wireless network. This method is quite general and applicable for finite and infinite network sizes, and it is flexible to encompass different system and propagation parameters such as large-scale fading, small-scale fading or even composite fading. We also investigate the behavior of these cumulants with respect to changes in the network size and fading distributions.

This paper focuses on turbo packet combining strategies for multi-relay-assisted systems operating over multiple-input-multiple-output (MIMO) broadband channel. We propose a frequency domain minimum mean square error (MMSE)-based turbo packet combining scheme where all slots received signals and their corresponding channel frequency responses (CFR)s are used to decode the data packet. We also provide an efficient recursive implementation way for the proposed scheme, and show that both its computational complexity and memory requirements are quite insensitive to the number of relays in the system. For the special case of cooperative automatic repeat request (ARQ) systems, we introduce an adaptive packet combining algorithm that enable to reduce the receiver implementation cost. Block error rate (BLER) performance are provided to demonstrate the gains offered by the proposed combining scheme over the conventional soft information-based combining.

Enabling unsubscribed Secondary User (SU) access through Dynamic Spectrum Access (DSA) techniques provides a huge opportunity to the Wireless Service Providers (WSPs) for achieving efficient radio spectrum usage as well as gain additional profits. This paper presents a novel and dynamic SU pricing model for implementation by the WSPs at their Base Stations (BSs) based upon a BS-centric distributed framework, that allows the SU price to vary dynamically with the changing radio spectrum usage at the BS. Assuming high competition among regional WSPs, a non-cooperative competition structure is considered among the WSPs, where neither information nor resources are shared among the WSPs. A simple single cell scenario easily scalable to multiple cells achieving competitive yet dynamic SU pricing among the BSs of two WSPs, is presented in this paper. The final SU price is seen to depend on the spectrum utilization at the BS, the wireless channel, and the price charged by WSPs to their PUs.

In this paper we propose a novel approach to obtain the optimal operating point of the spectrum sensing in overlay spectrum sharing. The operating point of the spectrum sensing is specified by the receiver operating characteristic (ROC) curve which is a fundamental spectrum sensing behavior that relates its false alarm and missed detection probabilities. Our objective is to maximize the ergodic capacity of the secondary service in the presence of inaccurate spectrum sensing. We formulate an optimization problem to obtain the optimal transmission power strategy and ROC operating point of the spectrum sensing. Constraints of the considered optimization problem include the secondary service maximum transmission power as well as the maximum primary service collision probability. Two iterative sub-optimal schemes are then proposed to find the solutions of the optimization problem. Numerical results show that both proposed schemes quickly converge after a few iterations. Moreover, it is observed that the proposed schemes significantly outperform other conventional approaches.

We study a dynamic inter-cell interference coordination scheme formulated using a novel binary integer linear programming optimization technique. The scheme aims to maximize utility, where different utility measures have been defined to model varying levels of user fairness. The formulated problem is decomposed into a number of smaller sub-problems and solved iteratively to ease the computational complexity. We observe the cell-edge and sector throughput as well as user fairness and compare with a reference scheme, where interference coordination is not used. It is observed that the proposed scheme always outperforms the reference scheme in terms of cell-edge and sector throughput as well as in fairness.

Various standardization activities leading to 4G and beyond networks have considered the synergy of OFDMA and multihop relaying thus giving way for the fixed relay station (FRS)-based radio access network. As such, the next-generation networks will comprise a plethora of performance enhancing devices among which is the plug-and-play nomadic relay station (NRS). It is essential for such dense and inevitably frequency reuse-aggressive networks to employ efficient mechanisms to mitigate the co-channel interference and to provide prudent energy utilization; this brings about the timely environmental concerns and the so-called green wireless initiative in designing future wireless networks. We present a novel joint power and subchannel allocation algorithm for the emerging OFDMA-based nomadic-augmented fixed relay networks. This NRS-directed algorithm performs adaptive power control (APC) within the autonomous opportunistic NRS medium access and channel reuse, using two different approaches. The APC mechanism is realized in an open-loop manner requiring no feedback from the WT. We demonstrate the power savings and user throughput improvement obtained through the joint scheme. We also identify a throughput-power saving trade-off in terms of the number of deployed FRSs. Through this work, the authors further establish their pioneering techniques for realizing the concept of NRS-augmented networks.

In constellation rearrangement (CoRe), the base-station and the relay use different constellations, with the same number of signal points, to communicate with the user terminal. In contrast to the existing CoRe techniques, which restrict the possible constellations, we propose generalized quadrature amplitude modulation (QAM) constellations. Since generalized CoRe schemes do not restrict constellations, they have the potential of outperforming all other CoRe schemes, with a complexity penalty in decoding. We pose an optimization to find the generalized QAM constellations, which minimize an upper bound on the uncoded symbol error rate (SER). However, since the optimization is not convex, it is not possible to find constellations with globally minimum SER in a reasonable time. Nevertheless, we use a convex solver to find constellations, which are local minima to the optimization. We input the best known restricted CoRe constellations as starting points to the solver to find constellations, which are guaranteed to improve SER. We demonstrate the significant gains achieved by the proposed CoRe scheme with simulations.

Providing ubiquitous very high data rate coverage in next generation wireless networks is a formidable goal, requiring cost-effective radio access network (RAN) devices, such as multi-user enabled amplify- and-forward (AF) relays, and fair radio resource management (RRM). To further this goal, we investigate multi-user enabled AF relays which multiplex user's data in orthogonal frequency division multiple access (OFDMA). These relays are cost-effective, simpler to implement, and introduce less delay in comparison to other relay based routers. We devise a generalized proportionally fair (GPF) RRM framework for multi-user enabled AF relays. In GPF scheduling a single parameter is used to gradually change schedules from throughput optimal to proportionally fair. We formulate the GPF scheduling problem and due to its complexity devise a low complexity heuristic to solve it. We evaluate the performance of the heuristic with extensive simulations and show that the heuristic performs well.

We investigate the problem of multi-user radio resource allocation for orthogonal frequency division multiple access (OFDMA) amplify-and-forward (AF) relays. In the single-user case, the problem reduces to the well know assignment problem, which maximizes the user rate. For the multi-user case we devise a resource allocation algorithm to achieve max-min fairness. We find max-min fairness since it can provide almost flat ubiquitous coverage. We start by formulating a convex optimization, which takes a parameter that asymptotically makes the optimization produce max-min fair rates. Since the optimization is a convex problem, we are able to devise a sub-optimal gradient-based algorithm to solve it quickly. Simulations show that the algorithm achieves results very close to the optimum solutions due to its gradient origins.

As the number of wireless devices sharing a radio band increases, so does the number N of potential co-channel interferers. The receiver performance is then strongly dependent on the total received interference power. While the statistics of this power have previously been studied under the channel assumption of independent shadowing, it is easy to show that for large N the correlation among the shadowing paths cannot be neglected. While this correlation may be simulated by matrix decomposition, we show that an alternative approximately equivalent algorithm using shadowing fields can achieve simulation performance that scales much better with N and has additional advantages.

In this paper we study the problem of relay-enhanced cell (REC) coverage for which relay location is optimized to maximize the achievable REC radius. The problem is investigated for both Rayleigh and Rician relay fading channels, under a pre-determined user's outage probability constraint. We propose a statistical approach to formulate the problem and develop an optimization algorithm for it. The analytical derivation is justified by numerical simulations.

In a spectrum overlay system, the secondary users (SUs) with cognitive radio capability need to detect the presence of primary users promptly and reliably in order to prevent excessive interference. Likewise, to make full use of the available spectra, such systems have to attain low false alarm probability. Having a high detection probability while maintaining a low false alarm probability is challenging as these are conflicting goals; therefore, an appropriate tradeoff needs to be determined. In this paper we propose a "Hierarchical and Adaptive Spectrum Sensing (HASS)" solution to multi-hop cellular networks, which is based on cooperative sensing and soften hard detection fusion mechanisms with one-bit overhead. SUs that are able to make local detection decisions either directly report their one-bit decisions to the cognitive radio base station or they relay their decisions to other favorable SUs based on the states of their reporting channels. If SUs can not make detection decisions, they relay the observed signals to other favorable SUs for further processing. Simulation results show HASS solution improves spectrum sensing performance in multi-hop cellular networks.

The generalized-K (Gamma-Gamma) composite fading model has been proposed in the past to represent the statistics of the instantaneous power of target and clutter scattering in radar systems. Recently, this model was used in wireless communications as an alternative to the analytically intractable lognormal-based composite fading models. In this paper, a simple approach is proposed to approximate the probability density function of the sum of independent generalized-K random variables by another generalized-K one using the moment matching method. The numerical evaluations show that the proposed approximation of the sum distribution is sufficiently accurate for realistic scenarios in wireless channels. Furthermore, the applications of the obtained closed-form expressions to the performance analysis of distributed antenna systems are presented.

Previous works studied the effect of many system parameters on spectrum sharing opportunities where secondary users access the spectrum of primary users. However, a parameter that has received little attention is the spatial size of the field of secondary users. Usually, the field size is assumed to be infinite. Using results developed for infinite fields might be too pessimistic leading to missing spectrum sharing opportunities. This paper studies the effect of the field size on spectrum sharing opportunities. We verify that asymptotic results obtained for infinite fields are applicable for finite but relatively large fields as well, i.e., when the radial depth of the field is much greater than the minimum distance to the primary user. We demonstrate that in some cases, however, asymptotic results are too pessimistic hiding some spectrum sharing opportunities. Moreover, the paper shows that in certain situations a small reduction in the field size may create spectrum sharing opportunities while in certain other situations a huge increase in the field size may not eliminate spectrum sharing opportunities. Our results also suggest the possibility of a secondary network to concurrently share the spectrum with a primary user without the need for spectrum sensing techniques or other cognitive radio functionalities.

The requirements of IMT-Advanced dictate innovations in radio resource management, including cross-layer optimization of radio resource allocation, inter-cell coordination, and interference mitigation strategies, dynamic and aggressive resource reuse, as well as state-of-the-art spatial signal processing. All of these technologies must be supported by an advanced radio access network capable of exploiting each ideally to their fullest extent. This paper proposes a novel radio access network architecture that we refer to as Shared Relay Segmentation (SRS) cellular architecture. While the concept of SRS may be implemented in a generic cellular multi-hop network, we focus on attributes of the SRS cell architecture that make it attractive for adoption in 4G networks such as WiMAX. There are three main contributions in this work: (a) We describe a novel and applique multi-hop cellular architecture requiring on average a single shared infrastructure relay station per WiMAX sector. (b) We develop a multi-hop fractional frequency reuse strategy derived from the WiMAX segmentation plan. (c) We develop an optimal centralized radio resource allocation scheme, based on the Hungarian algorithm, enabling adaptive non-transparent cooperative relaying in the downlink access zone of a WiMAX sector. We demonstrate through system simulations how an SRS WiMAX cellular architecture improves both sector coverage and capacity over conventional WiMAX systems. We use system parameters and performance metrics defined for evaluation of IEEE 802.16j/m systems as reference. Furthermore, we speculate that the SRS cellular architecture is suitable for applique enhancement of existing WiMAX deployments, enabling incremental CAPEX expenditure by network operators where driven by service demand.

The provision of very high data rates in a ubiquitous manner throughout the service area is a great challenge for 4G and beyond-4G wireless networks. Towards that end, the deployment of fixed relays by the operators has become an accepted radio access network concept in various standardization activities including LTE-A and 802.16j. It is envisaged that next-generation networks will comprise a plethora of wireless relay stations. Worthy of mention is the plug-and-play type of relay known as nomadic relay. We devise novel radio resource management (RRM) schemes to facilitate the operation of fixed relay stations (FRSs) and nomadic relay stations (NRSs) in OFDMA-based multicellular networks. Two schemes of different decentralization levels are devised and classified as distributed and semi-centralized. A novel user-based dynamic routing or link selection that significantly reduces the feedback overhead is employed. We develop methods by which the NRSs act autonomously to acquire radio resources without relying on a central entity. NRS operation is the same in the two schemes and can be extended to any other OFDMA-based RRM scheme. Through the asynchronous opportunistic medium access of the NRSs, smart and opportunistic intra-cell channel reuse is attained. This is different from the static intra-cell reuse patterns often adopted in literature. Furthermore, we introduce a method to enable the cooperation between an NRS and a serving FRS to assist a troubled wireless terminal (WT). We thus establish the concept of nomadic relay-augmented fixed relay networks. To the extent of our knowledge of the literature, no other work has undertaken this task so far.

In this paper, we consider DS-CDMA/OFDM spectrum sharing systems and obtain the achievable capacity of the secondary service under different subchannel selection policies in the fading environment. Subchannel selection policies are divided into two categories: uniform subchannel selection, and nonuniform subchannel selection. Uniform subchannel selection is preferred for cases where a priori knowledge on subchannels state information is not available at the secondary transmitter. For cases with available a priori knowledge on subchannels state information, we study various nonuniform subchannel selection policies. In each case, we obtain the optimum secondary service power allocation and the corresponding maximum achievable capacity. Then we present results on the scaling law of the opportunistic spectrum sharing in DS-CDMA/OFDM systems with multiple users. Numerical results show that the optimal subchannel selection is based on the minimum value of the subchannel gain between the secondary transmitter and the primary receiver.

In this paper, we analyze the achievable capacity of the secondary service for overlay and underlay access strategies. We then propose a novel mixed access strategy in which in contrast to the underlay strategy, the secondary service transmits during the idle periods without considering the interference threshold constraint. In contrast to the overlay strategy, mixed strategy makes transmission during the busy periods with a probability p_a subject to satisfying the interference threshold constraint. Parameter p_a is a secondary service parameter, which can be adjusted based on the spectrum status. Moreover, we show that the secondary service can adjust p_a to select appropriate access strategy with the objective of maximizing the achieved capacity based on the interference at the secondary service receiver, I, imposed by the primary service transmitter. The proposed spectrum-sharing technique developed in this paper based on I significantly reduces the system complexity comparing to the system in which for spectrum sharing, the imposed interference at the primary receiver is required. We further suggest a simple power allocation scheme for the mixed strategy that its achieved capacity is very close to the maximum achievable capacity of the secondary service.

In wireless channels, multipath fading and shadowing occur simultaneously leading to the phenomenon referred to as composite fading. The use of the Nakagami probability density function (PDF) to model multipath fading and the Gamma PDF to model shadowing has led to the generalized-K model for composite fading. However, further derivations using the generalized K PDF are quite involved due to the computational and analytical difficulties associated with the arising special functions. In this paper, the approximation of the generalized-K PDF by a Gamma PDF using the moment matching method is explored. Subsequently, an adjustable form of the expressions obtained by matching the first two positive moments, to overcome the arising numerical and/or analytical limitations of higher order moment matching, is proposed. The optimal values of the adjustment factor for different integer and non-integer values of the multipath fading and shadowing parameters are given. Moreover, the approach introduced in this paper can be used to well-approximate the distribution of the sum of independent generalized-K random variables by a gamma distribution; the need for such results arises in various emerging distributed communication technologies and systems such as coordinated multipoint transmission and reception schemes including distributed antenna systems and cooperative relay networks.

Interference management has been a key concept for designing future high data-rate wireless systems that are required to employ dense reuse of spectrum. Static or semi-static interference coordination based schemes provide enhanced cell-edge performance but with severe penalty to the overall cell throughput. Furthermore, static resource planning makes these schemes unsuitable for applications in which frequency planning is difficult, such as femtocell networks. In this paper, we present a novel dynamic interference avoidance scheme that makes use of inter-cell coordination in order to prevent excessive inter-cell interference, especially for cell or sector edge users that are most affected by inter-cell interference, with minimal or no impact on the network throughput. The proposed scheme is comprised of a two-level algorithm - one at the base station level and the other at a central controller to which a group of neighboring base stations are connected. Simulation results show that the proposed scheme outperforms the reference schemes, in which either coordination is not employed (reuse of 1) or employed in a static manner (reuse of 3 and fractional frequency reuse), in terms of cell edge throughput with a minimal impact on the network throughput and with some increase in complexity.

Relaying and orthogonal frequency division multiple access (OFDMA) are the accepted technologies for emerging wireless communications standards. The activities in many wireless standardization bodies and forums, for example IEEE 802.16 j/m and LTE-Advanced, attest to this fact. The availability or lack thereof of efficient radio resource management (RRM) could make or mar the opportunities in these networks. Although distributed schemes are more attractive, it is essential to seek outstanding performance benchmarks to which various decentralized schemes can be compared. Therefore, this paper provides a comprehensive centralized RRM algorithm for downlink OFDMA cellular fixed relay networks in a way to ensure user fairness with minimal impact on network throughput. In contrast, it has been observed that pure opportunistic schemes and fairness-aware schemes relying solely on achievable and allocated capacities may not attain the desired fairness, e.g., proportional fair scheduling. The proposed scheme is queue-aware and performs three functions jointly; dynamic routing, fair scheduling, and load balancing among cell nodes. We show that the proposed centralized scheme is different from the traditional centralized schemes in terms of the substantial savings in complexity and feedback overhead.

Composite fading takes place in several communication channels due to the random variations of the local average power of the received multipath-faded signal. The generalized-K (gamma-gamma) probability density function (PDF) has been proposed recently to model composite fading in wireless channels. However, further derivations using the generalized-K PDF have shown to be quite involved due to the computational and analytical difficulties associated with the arising special functions. In this paper, the approximation of the generalized-K PDF by a gamma PDF using the moment matching method is explored. As expected, matching positive and negative moments leads to a better approximation in the upper and lower tail regions, respectively. However, due to arising limitations for small values of the multipath fading and shadowing parameters, and the higher level of accuracy sought, the use of an adjustable form for the expressions of the approximating gamma PDF parameters, obtained by matching the first two positive moments, is devised. The optimal values of the adjustment factor for different integer and non-integer values of the fading and shadowing parameters are given. The introduced approximation may simplify performance analysis in distributed antenna systems (DASs), network MIMO, multihop relay networks, radar, and sonar systems.

The provision of very high data rates in a ubiquitous and fair manner throughout the service area is a great challenge for 4G and beyond-4G wireless networks. Towards that end, the deployment of fixed relays by the operators has become an accepted radio access network concept in various standardization activities including LTE-A and 802.16m. In this paper, we present a fair resource allocation scheme that can significantly reduce the co-channel interference and improve spectrum utilization in OFDMA-based multi-cellular networks enhanced by fixed relays. The major goal is to obtain an effective throughput-fairness trade-off through the combination of dynamic resource allocation, in-cell routing, traffic diversity, and history of users' past service. The proposed scheme achieves substantial fairness, throughput, and latency improvements in comparison to the reference schemes as well as to another scheme proposed, earlier by the authors. Numerical results indicate that the proposed scheme provides an almost ubiquitous high-data-rate service to users, irrespective of their locations (including cell-edge users), with a minimal sacrifice in the overall network throughput.

We propose a new method for calculating a tight approximation to the distribution of the sum of independent lognormal random variables. We make use of a three-parameter modified-power-lognormal distribution function as the approximating distribution. We use theoretical results from our previous work on the tails of the distribution of the sum of lognormals to match the slope of the modified-power-lognormal function at both tails. This would not have been possible with many of the recently-proposed distribution functions, which do not behave properly in the tails. We then also use moment-matching to find the best curve match. Our method is mostly closed-form, requiring only one simple numerical integral. We compare our method with those in literature in terms of complexity and accuracy. We conclude that our method is more accurate than the simple (closed-form) methods, and much simpler to understand and implement than the more accurate methods which rely heavily on numerical integration.

Relaying and orthogonal frequency division multiple access (OFDMA) are the accepted technologies for emerging wireless communications standards. The activities in many wireless standardization bodies and forums, for example IEEE 802.16 j/m and LTE-Advanced, attest to this fact. The availability or lack thereof of efficient radio resource management (RRM) could make or mar the opportunities in these networks. This paper therefore provides a comprehensive RRM algorithm for OFDMA-based multi-cellular fixed relay networks in a way to ensure fairness among users with minimal impact on the network throughput (in contrast, pure opportunistic RRM techniques always favor users with good channel conditions). Unlike the majority of works in the literature, our proposed scheme is queue-aware and jointly performs routing, fair scheduling, and load balancing among cell nodes. The routing strategy has inherent learning ability and it dynamically converges to better routes.

We propose a single carrier joint frequency domain equalization and interference cancellation (FDE-IC) with diversity combining for different relaying schemes. We consider amplify-and-forward (AF) and ACK/NACK-aided decode-and-forward (DF) modes over multiple-input multiple-output (MIMO) frequency selective relay channels. During the first time slot, the source transmits to both the relay and destination. In the second slot, the relay transmits the signal packet to the destination in the case of AF mode, while in DF the data block is transmitted either by the relay or the source depending on the ACK/NACK feedback. By deriving an equivalent source-relay-destination (S → R → D) channel for the AF mode, we propose an integrated iterative minimum mean square error (MMSE)-FDE-IC-aided transmission combining scheme where reception over multiple slots is viewed as virtual antennas. We also derive the joint combining scheme in the case of ACK/NACK-aided DF. Block error rate (BLER) performance is evaluated for different system settings.

Cooperative relaying introduces spatial diversity through the creation of a virtual antenna array. The vast majority of the research in digital cooperative relaying assumes the modulation level used by both the source and relay to be the same. This assumption does not necessarily hold when adaptive modulation is implemented. In conventional selection combining, the branch with the highest SNR is chosen; we refer to this scheme as SNR-based selection combining (SNR-SC). In this paper, we introduce BER-based selection combining (BER-SC), as an alternative to SNR-SC, to be used in cooperative communications when a relay may use a modulation level different than that of the source. We provide BER performance analysis for the SNR-SC and BER-SC schemes and show that BER-SC significantly outperforms SNRSC, without any increase in complexity. Moreover, we analytically quantify the gain achieved by using BER-SC over SNR-SC through asymptotic approximation. We note that BER-SC and SNR-SC schemes are identical when the received signals belong to the same modulation level.

Using the Nakagami probability density function (PDF) to model multipath fading and the Gamma PDF to model shadowing, in a wireless channel, has led to a closed-form expression for the composite fading PDF, known as the generalized-.fi" PDF (also called Gamma-Gamma PDF). However, further derivations have shown that the cumulative distribution function (CDF) and the characteristic function of the generalized-K PDF contain special functions that are involved to handle. In this paper, an approximation of the generalized-K PDF by the familiar gamma PDF is introduced. The parameters of the approximating Gamma PDF are computed using the moment matching method. The accuracy of this approximation in the lower and upper tail regions is enhanced by adjusting the parameters of the approximating gamma distribution in each region. The CDF and the complementary CDF plots show that this approximation is sufficiently accurate for both integer and non-integer practical values of the multipath fading and shadowing parameters. The region-wise approximation obtained by the adjusted moment matching method is used to well- approximate the PDF of the sum of identically and independent generalized-K random variables. Applications of the obtained results arise in distributed antenna systems (DASs), cooperative relay networks, radar, and sonar systems.

This paper derives bounds on the diversity- multiplexing tradeoff of wireless relay networks with arbitrary link connectivity between cooperating terminals. The derived bounds are applicable when there are single and multiple antennas per terminal. Two classes of relaying method are analyzed, those requiring all cooperating terminals to correctly decode the transmitted signal in order for the destination to correctly decode (comprehensive decoding), and those requiring only a subset of cooperating terminals to correctly decode the transmitted signal in order for the destination to correctly decode (destination decoding). It is shown that the diversity- multiplexing tradeoff of comprehensive decoding is constrained by the minimum number and arrangement of incident inter- terminal antenna links across all receiving terminals in the network, and that the diversity-multiplexing tradeoff of destination decoding is constrained by the minimum number and arrangement of incident inter-terminal antenna links across all cut sets in the network. Results for the complexity of the involved minimization algorithms are provided.

The investigation of co-channel interference mitigation techniques (such as, interference cancellation through receiver processing, interference randomization by frequency hopping, and interference avoidance through resource usage restrictions imposed by frequency and power planning) has become a key focus area in achieving dense spectrum reuse in next generation cellular systems such as 3GPP LTE, LTE- advanced, and WiMAX. In this paper, we propose an interference avoidance scheme for LTE downlink that uses dynamic inter-cell coordination facilitated through X2 interface among neighbouring evolved UTRAN nodeBs (eNBs, i.e., LTE base stations). Proposed scheme is evaluated by extensive simulations and compared with a number of reference schemes available in the literature. It has been observed that the proposed scheme attains superior performance in terms of cell-edge and sector throughput compared to those in the reference schemes.

Link adaptation techniques aim to maximise the quality of service and resource utilisation in wireless networks. However, fairness must be taken into consideration, particularly, in low-mobility environments where the channel dynamic variation is small. The authors propose and analyse three link adaptation techniques [using joint power control (PC) and adaptive coding and modulation (ACM)] for fairness enhancement. In the first technique, called aggregate throughput maximisation with fairness constraint, the authors formulate the fairness problem as a constrained optimisation problem where the authors try to maximise the aggregate throughput subject to the throughput fairness constraint. In order to solve the optimisation problem, the authors convert the constrained optimisation problem to an unconstrained optimisation one using the penalty method. Then, the unconstrained optimisation problem is solved using the steepest descent technique. The second techniques, called individual throughput balancing, tries to equalise the individual throughput by using a higher throughput level for disadvantaged users and using a lower throughput level for advantaged users. Finally, the third technique, called adaptive virtual maximum power constraint, uses virtual maximum power cap, which is lower than the real maximum power cap. The virtual maximum power cap of each user is variable and it adapts based on the user's individual throughput to compensate disadvantaged users. The authors analyse the three proposed techniques in terms of the throughput fairness and the throughput efficiency and compare them with three basic link adaptation techniques (PC, ACM, and joint PC and ACM). The three proposed techniques are shown to be able to enhance the fairness with different degrees and with different levels of aggregate throughput degradation.

In this letter the performance of multiple relay channels is analyzed for the situation in which multiple antennas are deployed only at the relays. The simple repetition-coded decode and-forward protocol with two different antenna processing techniques at the relays is investigated. The antenna combining techniques are maximum ratio combining (MRC) for reception and transmit beamforming (TB) for transmission. It is shown that these distributed antenna combining techniques can exploit the full spatial diversity of the relay channels regardless of the number of relays and antennas at each relay, and offer significant power gain over distributed space-time coding techniques.

We prove that the distribution of the sum of N identically distributed jointly lognormal random variables, where all pairs have the same strictly positive correlation coefficient, converges to a lognormal with known parameters as N becomes large. We confirm our theorem by simulations and give an application of the theorem.

In this paper, a two-hop cooperative multi-relay communication network is considered. Selection relaying schemes are attracting considerable attention due to their prudent bandwidth utilization and ability to provide full diversity. The recent developments in selection relaying (SR) have largely focused on information theoretic analyses such as outage performance. Some of these analyses are accurate only in high SNR regimes. This paper provides exact outage and capacity performance expressions for selection relaying and tight approximation over a sufficiently wide range of SNR regimes for selection cooperative relaying. The outage capacity for SR is also provided. The motivation for this work is that practical systems operate at far lower SNR values than those supported by the high SNR analysis. Therefore, designers should be able to evaluate system performance to a reasonable degree of accuracy over practical SNR values. Simulations are used to corroborate the analytical results and close agreement is observed.

In this letter, a joint transmit scheduling and dynamic sub-carrier and power allocation method is proposed to exploit multi-user diversity in downlink packet transmission in an OFDM wireless network with mixed real-time and non-real-time traffic patterns. To balance efficiency and fairness and to satisfy the QoS requirements of real-time users, we utilize a utility-based framework and propose a polynomial-time heuristic algorithm to solve the formulated optimization problem. The distinguishing feature of the proposed method is that it gives in one shot, the transmission scheduling, the sub-carriers assigned to each user, and the power allocated to each sub-carrier, based on a fair and efficient framework while satisfying the delay requirements of real-time users.

When utilizing spectrum sharing in wireless channels, a secondary service may access the spectrum allocated to the primary service while this frequency band is under-utilized. The availability of the frequency band to the secondary user is a function of the activity of the users in the primary network. In this paper, we analyze the achievable capacity of the secondary service which employs opportunistic spectrum access (OSA) over a fading environment based on the primary network activity. We categorized OSA methods into Access Limited OSA (AL-OSA), and interference limited OSA (IL-OSA) schemes. In AL-OSA the spectrum is shared with the secondary service in circumstances in which the primary service is totally inactive however, in IL-OSA access to the spectrum is allowed subject to an interference threshold. For both cases we develop analytical frameworks to analyze the impact of the primary network activity on the achievable capacity of the secondary service. Simulation results confirm our analysis and also show that in cases where higher activity is in the primary network, IL-OSA is the more appropriate OSA method. For a less active primary network, AL-OSA is shown to performs better with respect to the achievable capacity.

In digital cooperative relaying, signals from the source-destination and relay-destination links are combined at the destination to achieve spatial diversity. These signals may not necessarily belong to the same modulation scheme due to the varying channel qualities of the two links. Recently, we have proposed the "soft-bit maximum ratio combiner" (SBMRC) as a low complexity diversity combining scheme for signals with different modulation levels. SBMRC exhibits BER performance that is very close to the optimal maximum likelihood detector (MLD), but with much reduced complexity. In this paper, we revisit SBMRC and provide tight lower bound for the BER performance. Since SBMRC has BER performance slightly inferior to MLD, the derived lower bound can also be used as a good approximation for the BER performance of MLD.

This paper considers two-hop cooperative relaying using multiple relays and proposes a threshold based relay selection protocol. In this protocol the relays are selected among those having received SNR higher than a threshold value. The relay selection is performed by the destination based on the received SNRs at the destination during the last hop. The exact bit error rate of this protocol is derived and it is shown that it achieves full diversity order. Unlike some other full diversity achieving protocols in the literature, the requirement that the exact/average SNRs of the source-relay links be known at the destination is eliminated using an appropriate SNR threshold.

We describe and analyze minimum path difference (MPD), a metric to improve threshold-based selection decode-and-forward (SDF) protocols in cooperative wireless networks with channel coding. By observing the decoding process, MPD provides a more frequent channel quality estimation than CRC or SNR and, thus, allows SDF to forward only the parts of a message that are likely to be correct. In this paper, we describe MPDpsilas efficient integration into Viterbi decoders and SDF protocols, analyze its accuracy and threshold selection, and study its end-to-end Bit Error Rate in practical cooperative WLANs where ideal block fading and ideal channel codes cannot be assumed. Our studies validate that, unlike all prior methods, in these practical scenarios, MPD significantly improves SDFpsilas performance and experiences only a marginal performance penalty if suboptimal, constant thresholds are selected.

Interest in selection relaying is growing. The recent developments in this area have largely focused on information theoretic analyses such as outage performance. Some of these analyses are accurate only at high SNR regimes. In this paper error rate analyses that are sufficiently accurate over a wide range of SNR regimes are provided. The motivations for this work are that practical systems operate at far lower SNR values than those supported by the high SNR analysis. To enable designers to make informed decisions regarding network design and deployment, it is imperative that system performance is evaluated with a reasonable degree of accuracy over practical SNR regimes. Simulations have been used to corroborate the analytical results, as close agreement between the two is observed.

Massive deployments of wireless sensor networks (WSNs) are expected in near future. In one of the most likely scenarios, these WSNs would share a licensed frequency band with a primary user. So, it is essential to understand the behavior of the interference generated by a WSN towards the primary user. This paper provides an asymptotic analysis of the average interference power generated by a WSN. The analysis is extended to a special but important shape of a sensor field. This shape can be used to provide an upper bound of the average interference power generated by any sensor field with an arbitrary shape. The paper shows that the expansion of the sensor field does not necessarily cause an increase in the average interference power. For most practical values of path loss exponent, the average interference power asymptotically approaches constant levels with the increase in the field size provided that the minimum distance from the field to the primary user is fixed. The paper provides expressions for these constants. Moreover, results indicate that a key parameter in determining the average interference power is the ratio of the radial depth of the field to the minimum distance from the field to the primary user. Also, this paper illustrates how a WSN can be equivalently represented by a single virtual node producing the same level of average interference power.

In this paper we present an efficient cross-layer scheduling algorithm designed for resource allocation in downlink of 4G wireless multicarrier networks. This study focuses on the mechanisms of efficiency, fairness, as well as quality of service (QoS) provisioning and algorithm development for resource allocation in multiuser frequency-selective fading environments. The performance of the proposed scheme is compared to that of the score based (SB) scheduling, which is a variation of the proportional fair (PF) algorithm (the most widely adapted opportunistic scheduling technique), in the presence of interference. It is observed from simulation results that the proposed scheme provides better fairness in terms queuing delays, and dropped packets for various loading factors while the throughput remains comparable. A gain in the performance of cell edge users is also observed for the proposed scheme, this may result in substantial savings in the deployment cost since fewer base stations (BS) will be needed to cover regions.

Adaptive token bank fair queuing (ATBFQ) algorithm has been proposed as a cross-layer scheduling technique for 4G wireless systems recently. This algorithm takes higher layer quality of service (QoS) attributes such as priorities, interflow fairness, and delay constraints into account. By selecting the user terminals (UTs) in a certain prioritized manner derived from QoS attributes, the performance of the UTs, suffering from high interference and/or shadowing in particular, can be improved. The ATBFQ algorithm has been tested in a multicell environment in the presence of intercell interference by comparing with reference Score Based (SB) and Round Robin (RR) algorithms. In this paper, we further analyze ATBFQ primarily with regard to fairness along with other performance metrics accessed in a more elaborate system considering varying interference and loading conditions. Furthermore, an adaptive method for the allocation of resources is proposed for ATBFQ parameter selection, and is shown to have better performance in various loading conditions. It is observed from simulation results that ATBFQ with adaptive parameter selection outperforms the reference schemes in terms of queuing delay and UT throughput for different network loading cases.

This paper considers resource allocation in OFDM wireless networks with mixed real-time and non-realtime traffic patterns. We employ a utility-based framework to balance efficiency and fairness, while satisfying QoS requirements of real-time users. A utility function is assigned to each user over each sub- carrier; this indicates the transmission priority for the user/sub-carrier pair based on the achievable rate and experienced delay, subject to delay and power constraints. The proposed scheduler gives in one shot the sub-carrier and power allocation, plus transmission scheduling for each time-slot. Simulation results indicate that while satisfying real-time users' delay requirements, the proposed method achieves a significant performance improvement in terms of overall throughput and fairness.

In this paper, we analyze DS-CDMA/OFDM spectrum sharing system based on opportunistic spectrum access (OSA). In this system, the primary network air interface is based on DS-CDMA, in which the system performance is limited by a maximum total received interference. The total interference, among other things, is a function of the primary users' communication activity. In OSA, part of this interference is considered to be created by the secondary service, this portion is called interference threshold. Decreasing the activity of the primary users results in a higher interference threshold, and at the same time, creates less interference at the secondary service receiver; thus the achievable capacity of the secondary service is increased. In this paper, we analytically obtain the maximum achievable capacity of the secondary service over fading channel. Our analysis reveals the dependency of the achievable capacity and the users' activity in the primary network. A direct consequence of our analysis is that exploiting the temporal variations of the available radio resource caused by the users' activity in the primary network can significantly increase the maximum achievable capacity. Simulation results confirm our analysis.

OFDMA subchannel allocation has been well-studied. However, most of the available literature considers a single cell system without cochannel interference. We present a novel intercell interference avoidance scheme, in which the downlink OFDMA subchannels are allocated through intercell coordination in a multicell environment. The proposed scheme not only aims to achieve maximized network throughput but also focuses on providing improved throughput for cell or sector edge users that are most affected by intercell interference. Enhanced cell edge performance may result in fewer base stations needed to cover a region; this, in turn, may yield substantial savings in deployment cost. The scheme is comprised of two different algorithms; one at the base station level, and the other at a central controller to which a group of neighboring base stations are connected. The performance of the proposed scheme is compared with that of a reference scheme in which coordination is not employed. It is observed from simulation results that the proposed scheme outperforms the reference scheme in terms of network and cell edge throughput.

In digital cooperative relaying, signals from the source-destination and relay-destination links are combined at the destination to achieve spatial diversity. These signals do not necessarily belong to the same modulation scheme due to the varying channel qualities of the two links. In this paper, we present novel and low complexity schemes for diversity combining of signals with different modulation levels. We start by developing the optimum solution as a maximum likelihood detector (MLD). Due to its high complexity, we propose two other receiver structures that we refer to as soft-bit maximum likelihood detector (SBMLD) and soft-bit maximum ratio combiner (SBMRC). The proposed schemes are simple bit-by-bit detectors and only 0.3 dB inferior to the MLD in performance. The SBMLD provides only marginal performance gain over SBMRC through the computation of the conditional probability density functions of the soft-bits. Consequently, the SBMRC is a more attractive and practical solution. The performance of SBMRC is compared to that of selection combining which is the current approach in the literature for combining signals with different modulations. The SBMRC, along with its simplicity, outperforms selection combining by almost 2 dB without bandwidth loss or the need for extra channel state information. The SBMRC scheme can be viewed as a more general form of the classical maximum ratio combiner (MRC).

This paper studies various cooperative diversity schemes for orthogonal frequency division multiple access (OFDMA)- time division duplex (TDD) based two-hop cellular networks in low mobility scenarios, where the instantaneous channel state information is available at the base station. A user scheduling and radio resource allocation technique is developed in order to efficiently integrate various cooperative diversity schemes for the emerging IEEE 802.16j based systems. The analysis of the system with this scheduler shows that a simple cooperative diversity scheme which dynamically selects the best scheme between conventional relaying and direct transmission is promising in terms of throughput and implementation complexity. The conventional relaying refers to the scheme where the destination relies solely on the signals received through the relay.

In uncoded cooperative wireless networks, error propagation due to the detection errors at the relay limits the performance of cooperative digital relaying. Threshold relaying is a simple and effective technique to mitigate error propagation without any reliance on the channel coding. This paper analyzes the asymptotic end-to-end (e2e) bit error rate (BER) of threshold digital relaying in a network with a single relay and links experiencing independent Rayleigh fading. It is shown that the optimal threshold that minimizes the e2e BER increases as log(SNR) as the average link signal-to-noise ratios are increased simultaneously. The resulting e2e BER decreases as log(SNR)/SNR2, thereby achieving dual diversity.

We examine the statistical distribution of the interference produced by a cluster of very many co-channel interferers, e.g., a sensor network, or a city full of active wireless devices and access points. We consider an arbitrary statistical interferer layout and consider the interference as experienced at a given point outside (and not immediately near to) the interferer area. We model the paths as experiencing power law attenuation and lognormal correlated shadowing. It has been shown in literature that adding correlation to the shadowing model can give qualitatively different (and probably more realistic) results. Our results are mostly analytical, with a small amount of numerical integration required. Whereas simulations of very many correlated interferers are very computationally heavy, our method's complexity is independent of the number of interferers, and its precision in fact improves when increasing the number of terms.

In multihop cellular networks (MCN), the user nodes can act as relays and forward other nodes' traffic to/from base stations. There are several advantages of MCN such as the improved signal quality and higher coverage. However, it is known that multihop relaying networks require extra radio resources. Therefore the performance of MCN depends to a great extent on the availability of adequate radio resources. The performance of a time division multiple access (TDMA)-based multihop fixed cellular network is analysed with highlighting the dependence of the system performance on the amount of available radio resources, namely, the number of frequency carriers. Results show that in a fixed cellular network, the multihop architecture significantly outperforms the traditional single-hop architecture in terms of the outage probability and throughput if an adequate amount of frequency carriers is available in the network. Otherwise, the multihop fixed cellular networks architecture loses its superiority and might even lead to performance degradation, particularly at high loading levels.

This paper studies selective relaying schemes based on signal-to-noise-ratio (SNR) to minimize the end-to-end (e2e) bit error rate (BER) in cooperative digital relaying systems using BPSK modulation. In the SNR-based selective relaying, the relay either retransmits or remains silent depending on the SNRs of the source-relay, relay-destination, and source-destination links. Different models assuming the availability of different sets of instantaneous and average SNR information at the relay are studied. For each model, the optimal strategy to minimize the e2e BER is a different threshold rule on the source-relay SNR, if the link SNRs are uncorrelated in time and space. Approximations for the optimal threshold values that minimize the e2e BER and the resulting performance are derived analytically for BPSK modulation. Using the derived threshold the e2e BER can be reduced significantly compared to simple digital relaying. By studying the performance under different models, it is shown that knowledge of the instantaneous source-destination SNR at the relay can be exploited. The gain from this knowledge is higher when the average source-destination SNR is large. However, knowledge of the instantaneous relay-destination SNR at the relay does not change performance significantly.

Threshold relaying is an effective technique to achieve cooperative diversity in uncoded cooperative wireless networks, which suffer from error propagation due to detection errors at the relays. This paper analyzes the asymptotic end-to-end (e2e) bit error rate (BER) of threshold digital relaying. A three node network with a source, destination and relay and with links experiencing independent Rayleigh fading is considered. It is shown that, as the average link signal-to-noise ratios (SNR) are increased simultaneously, the optimal threshold that minimizes the e2e BER increases as log(SNR). The resulting e2e BER decreases as log(SNR)/SNR2. Moreover, any threshold of the form log(cSNR), where c is a positive constant, achieves the same order of e2e BER as the one achieved by the optimal threshold and provides dual diversity. A value of c that performs very close to the optimal threshold is also proposed.

In this paper, we present a novel integer programming (IP) optimization formulation for downlink OFDM subchannel allocations in a multicell system and propose viable sub-optimal solutions. The schemes use dynamic intercell coordination in order to avoid or minimize dominant co-channel interference and achieve maximum network throughput by exploiting multicell multiuser diversity and interference avoidance gains. The performance of the proposed schemes is compared with that of reference round-robin schedulers without intercell coordination. It is observed from snapshot simulation results that the proposed schemes outperform the reference schemes in terms of throughput and quality of service (QoS) considerations.

In this paper we analyze the performance of multiple relay channels when multiple antennas are deployed only at relays. Specifically, we investigate the simple repetition-coded decode-and-forward protocol and apply two antenna combining techniques at relays, namely maximum ratio combining (MRC) on receive and transmit beamforming (TB). We assume that the total number of antennas at all relays is fixed to N. With a reasonable power constraint at the relays, we show that the antenna combining techniques can exploit the full spatial diversity of the relay channels and can achieve the same diversity multiplexing tradeoff as achieved by more complex space-time distributed coding techniques, such as those proposed by Laneman and Womell (2003).

Finding the distribution of the sum of lognormal random variables is an important mathematical problem in wireless communications, as well as in many other fields. While several methods exist to approximate this distribution, their performance tends to deteriorate in both tails. Finding a good overall fit remains an open problem. Other disadvantages of these methods are their complexity and, in some cases, their limitation to particular scenarios. In this paper we examine the sum of independent lognormal random variables with arbitrary parameters. We define the concept of best lognormal fit to a tail and show what it means in terms of convergence. We restate a known result about asymptotes to the higher tail of the distribution. To our knowledge, the lower tail has not yet been studied. We give a simple closed-form expression for an asymptote to the lower tail. We also show that known methods for finding the sum of lognormals use distribution functions that do not have this asymptotic behaviour in the tails. Our results are complementary to the existing knowledge, which together can combine to solve the problem of the sum of lognormals simply and exactly. We support our results by simulations.

This paper derives bounds on the diversity order and high SNR probability of outage behavior of wireless relay networks with arbitrary link connectivity between cooperating terminals. Single and multiple antennas per terminal are considered. Two general codebook generation schemes are analyzed and compared. Common codebook generation involves the source and all relays sharing a common randomly generated codebook, and encompasses many practical encoding schemes, including both repetition coding and space-time coding. Independent codebook generation involves the source and all relays employing independent randomly generated codebooks, and provides an information theoretic lower bound on the probability of outage of all achievable codebook generation schemes. The results indicate that although independent codebook generation does not offer any diversity gain over common codebook generation, it can offer a significant probability of outage improvement under certain conditions.

We study selective digital relaying schemes where the relay may choose to retransmit or to remain silent based on the qualities of the links between the source, relay and the destination. We first analyze a baseline scheme, called static relaying, where the relaying decisions are based only on the average signal-to-noise ratio (SNR) values of all the links. The second scheme, dynamic relaying, allows the relay to make decisions based on the instantaneous SNR of the source-relay link and average SNRs of the relay-destination and source-destination links. We show that, in dynamic relaying the optimal strategy to minimize the average end-to-end bit error rate is a threshold rule on the instantaneous SNR of the source-relay channel. In this case, the optimal threshold value is a function of average SNR of relay-destination and source-destination channels. We derive closed-form expressions for the optimal threshold and the bit error performance achieved by this threshold. We show that dynamic relaying can provide significant performance advantage over static relaying.

Space, cost, and signal processing constraints, among others, often preclude the use of multiple antennas at wireless terminals. This paper investigates distributed decode-and-forward fixed relays (infrastructure-based relaying) which are engaged in cooperation in a two-hop wireless network as a means of removing the burden of multiple antennas on wireless terminals. In contrast to mobile terminals, the deployment of a small number of antennas on infrastructure-based fixed relays is feasible, thus, the paper examines the impact of multiple antennas on the performance of the distributed cooperative fixed relays. Threshold-based maximal ratio combining (MRC) and threshold-based selection combining (SC) of these multiple antenna signals are studied and analyzed. It is found that the end-to-end (E2E) error performance of a network which has few relays with many antennas is not significantly worse than that which has many relays each with a fewer antennas. Obviously, the former network has a tremendous deployment cost advantage over the latter. It is also observed that the E2E error performance of a network in which the multiple antennas at relays are configured in SC fashion is not significantly worse than that in which MRC is used. For implementation, SC presents a significantly lower complexity and cost than a full-blown MRC. The analysis in this paper uses the versatile Nakagami fading channels in contrast to the Rayleigh model used in most previous works

This paper considers the ways that cooperating terminals can be connected to each other in wireless relay networks and the constraints imposed by the availability of different system resources. A framework is developed that exposes the relationship between constraints on available system resources and the achievable combinations of communication links between cooperating terminals. Cooperative connectivity models defined by the achievable combinations of links are derived, associated with their minimum cost constraint sets, and mapped to diversity techniques presented in the literature. The constraints considered are the available number of orthogonal relaying channels, the ability of terminals to diversity combine signals on a single common channel, the ability of terminals to diversity combine signals on orthogonal channels, the ability of terminals to transmit signals on multiple orthogonal channels, and the ability of terminals to cancel the effects of interhop interference.

In many scenarios the commonly assumed symmetry in multiple relay channels is unrealistic. Therefore, this paper, through analytical and simulation efforts, investigates asymmetric relay deployment where the links of cooperating nodes to destination experience unequal signal strength. An analysis of the cooperative error rate at the destination node in such networks is presented. Using the derived expressions for the cooperative error, in conjunction with the approaches used in earlier works, the end-to-end (E2E) performance of a two- hop network can be obtained. Moreover, the derived expression represents, in certain scenarios, a tight bound for the E2E error rate of the two-hop network such as when relay adopts threshold decode-and-forward strategy and/or multi-antenna processing to improve the reliability of its detection.

Fixed relays are expected to be a part of future infrastructure-based wireless networks. Besides coverage extension, such relays can form advanced architectures due to the flexibility in their power expenditure and physical size. This paper explores the potential benefits of multi-antenna relays for spatial multiplexing of independent data sources sending data to a common multi-antenna destination such as a base station or an access point. Overall, the system resembles a horizontally coded layered space-time architecture. In particular, we consider zero forcing decision feedback (ZF-DF) type MIMO receivers and study their outage performance under various (non-selective and selective) digital relaying protocols. For diversity relaying protocol, we propose two schemes, Joint ZF-DF and Parallel ZF-DF, for joint processing (combining and decoding) of the direct user signals and the signal from the relay. We show that with the proposed selective diversity relaying protocols and joint ZF-DF processing, the outage probability of the system can be decreased significantly.

We propose an interference management scheme for providing quality of service (QoS) in the absence of automatic repeat request (ARQ) in cellular networks augmented with fixed relays. The high packet error rate in a system with ARQ not only incurs additional packet delay, which is undesirable for real-time services, but also reduces link throughput because of the increased over-the-air signaling overhead and retransmissions. The proposed scheme strives for improvements in packet error rate and net throughput while maintaining acceptable delay through the use of inter-cell coordination. The inter-cell coordination uses backbone network for information exchange and thereby transfers over-the-air signaling overhead to the high-speed backbone network. In our scheme, a group of base stations, each of which is either a recipient of or a contributor to dominant interference, form an interferer group and exchange channel state information with each other. Based on this instantaneous channel quality information, the proposed scheme makes intelligent scheduling and routing decisions taking the interference into account. The performance of the scheme is compared with that of an uncoordinated scheme through extensive simulations. It has been observed that the proposed scheme achieves significant performance benefits in terms of packet error rate and net throughput.

In this paper using a utility-based approach, down-link packet transmission in a CDMA/TDMA cellular network is formulated as an optimization problem. A utility function corresponds to each packet served by a base-station that is an increasing function of the packet experienced delay and the channel gain, and a decreasing function of the base-station load. Unlike previous works, in this paper, the optimization objective is to maximize the total network utility instead of the base-station utility. We show that this optimization results in joint base-station assignment and packet scheduling. Therefore, in addition to multi-user diversity, the proposed method also exploits multi-access-point diversity and soft capacity. A polynomial time heuristic algorithm is then proposed to solve the optimization problem. Simulation results indicate a significant performance improvement in terms of packet-drop-ratio and achieved throughput.

Cooperative diversity through mobile terminals is faced with a number of deployment challenges such as security (as terminals have to detect partner's signals), incentive and coercion packages (as users have to be motivated to use their terminals to assist others), and huge capital investment (as terminals have to be modified to make them ready for cooperation). This paper discusses fixed relay-enabled user cooperation that are being considered in the WINNER project for deploying beyond 3G wireless communication networks. The schemes operate by engaging two users in cooperation without their knowledge. The schemes which can be viewed as an add-on to network facility have significant impact on the costs, security and end-to-end (E2E) performance of two-hop wireless networks. In addition, the proposed schemes can be used in a number of networks since current terminals do not require any modifications.

Maximizing the aggregate throughput of wireless cellular networks using link adaptation techniques (e.g. adaptive modulation and power control) has got a great deal of attention (e.g., [5]). However, the fairness of individual throughput allocation has not been well addressed yet. The fairness problem of link adaptation techniques has recently attracted some attention (e.g., [6, 9]) but the problem still needs deeper analysis and more efficient solutions. This paper proposes a new scheme that finds the optimal power and modulation level that maximizes the aggregate throughput while taking the individual throughput fairness into consideration. The problem is formulated as a constrained optimization problem and then converted to an unconstrained optimization one by including the constraint as a penalty term in the objective function. The unconstrained optimization problem is solved using the steepest descent technique. Results show that the proposed scheme is always able to achieve the fairness requirements with high aggregate throughput.

In this paper we present network-centric and user- centric routing mechanisms designed for the wireless world initiative new radio (WINNER) multi-hop cellular air interface. We study the pros-and-cons of each mechanism and describe the interactions between routing and main radio resource management (RRM) functionalities.

In this paper we study the multi-user diversity gain in the downlink of single-hop and multi-hop infrastructure-based networks. We propose a base-station coordinated cooperative relaying method, cooperative induced multi-user diversity relaying (CIMDR), to overcome the fundamental limitations on the average achieved net throughput per-user. In the proposed method, multi-user diversity is induced and then exploited in a 2-hop relaying scheme to improve per user achieved data throughput. We show that by using the proposed method, the throughput per-user and the packet-drop-ratio are significantly improved

Multiple antenna schemes can provide performance enhancement but the small size and cost constraints of wireless terminals preclude their use at these terminals. This paper investigates distributed fixed relays (infrastructure-based relaying) which are engaged in cooperation in a two-hop wireless network as a means of removing the burden of multiple antennas on wireless terminals. In contrast to mobile terminals, a small number of antennas can be deployed on infrastructure-based fixed relays. Hence, the paper investigates performance of the distributed cooperative multi-antenna fixed relays. Threshold maximal ratio combining and threshold selection combining of these multiple antenna signals are studied and analyzed. The following results are derived. For a given performance requirement, the multiple antennas at relays can significantly reduce the number of relays required in a network area, ultimately, reducing system deployment cost. In addition, threshold selection combining at the relays represents an excellent compromise between performance and system cost. Finally, the analysis performed in this paper uses the versatile Nakagami fading channel model

In this paper, we argue that while relays will surely be an essential component of future infrastructure-based wireless networks, their indiscriminate use is an inefficient way to allocate the limited radio resources available. The most efficient method to allocate the radio license is when the number of relays is minimized, while still achieving the desired coverage objectives. Under very general assumptions, we show that there exists an optimal number of relays which maximizes the aggregate spectral efficiency for wireless connections within a base station coverage area

We investigate microdiversity-augmented macrodiversity techniques in wireless communication networks. The setup consists of K widely separated access ports each carrying N antennas. The conventional selection macrodiversity (Scheme I) and two proposed methods (Scheme II and Scheme III) for port selection are investigated and their performance are compared. Scheme II utilizes the advancement in distributed antennas and radio-on-fiber technologies whereas Scheme III utilizes these technologies in a way similar to the soft handover of CDMA systems. After the port selection, the microdiversity uses n strongest signals (n les N) of the selected port for diversity combining if practical constraints preclude the use of all N signals. Analytical performance expressions for the probability of error and outage probability for Scheme I are developed. Simulations are used to investigate Scheme II and Scheme III and it is observed that these latter schemes exhibit performance superiority over the conventional macrodiversity selection method. The power savings obtained using the proposed architecture and new selection schemes could be used either for coverage extension or to increase capacity in wireless networks

Abstract This paper proposes a transmission‐scheduling algorithm for interference management in broadband wireless access networks. The algorithm aims to minimize the cochannel interference using basestation coordination while still maintaining the other quality of service (QoS) requirements such as packet delay, throughput and packet loss. The interference reduction is achieved by avoiding (or minimizing) concurrent transmission of potential dominant interferers. Dynamic slot allocation based on traffic information in other cells/sectors is employed. In order to implement the algorithm in a distributed manner, basestations (BSs) have to exchange traffic information. Both real‐time and non‐real‐time services are considered in this work. Results show that significant reduction in the packet error rate can be achieved without increasing the packet delay at low to medium loading values and with a higher but acceptable packet delay at high loading values. Since ARQ schemes can also be used for packet error rate reduction, we compare the performance of the proposed scheme with that of ARQ. Results indicate that although ARQ is more effective in reducing packet error rate, the proposed algorithm incurs much less packet delay particularly at medium to high loading. Copyright © 2006 John Wiley & Sons, Ltd.

In this paper we analyze the optimal number and locations of fixed radio relay stations forming multi-hop links in infrastructure-based wireless networks. Under the assumptions of using orthogonal channels for the hops and all links having the same average path loss exponent, we show using the spectral efficiency as a metric that the optimal relay locations are at equal intervals along the straight line between source and destination whenever multihop is to be utilized. We show that a single-hop fixed radio link can be efficiently (from a spectral efficiency perspective) replaced with the multi-hop link only if the single-hop SNR has a relatively low value. We introduce a "multi-hop criterion" to quantify the single-hop SNR value below which a more efficient multi-hop replacement exists. We also determine the optimal number of hops for the multi-hop links having relays disposed in straight line at equal intervals

The paper considers the impact of various system resource constraints on the connectivity and performance of relaying channels. This consideration is motivated by recent findings indicating that the performance of wireless relaying networks can be increased through the application of distributed spatial diversity techniques (e.g., multi-user diversity, multihop diversity, cooperative diversity) that rely on the mesh connectivity between wireless terminals. Relevant system resource constraints are introduced, constraint combinations are analyzed, and resultant system connectivity models are derived and compared via simulation. The constraints considered are the available number of orthogonal relaying channels, the ability of relays to diversity-combine signals, the ability of the destination to diversity-combine signals, the ability of receivers to diversity-combine signals on multiple orthogonal channels, and the ability of transmitters to transmit signals on multiple orthogonal channels. The analysis of these system resource constraints is applicable to both amplified (non-regenerative, amplify-and-forward) relaying and decoded (regenerative, decode-and-forward) relaying.

In this paper, we present a novel transmission scheduling scheme for fixed broadband wireless access (FBWA) networks. In order to mitigate high co-channel interference resulting from dense reuse of frequency, scheduling schemes often consider interference management issues as an essential part. To that end, a series of literature has been published recently, where a group of base stations form an interference group, and scheduling scheme deployed in the group allows only one base station to transmit at a time. As a result of time orthogonality in transmissions, the co-channel interference and hence the packet error rate can be reduced. However, prohibiting concurrent transmissions in these orthogonal schemes introduces higher end-to-end packet delay which might not be desirable for real-time traffic such as voice and video. Our proposed scheme aims to improve delay performance as well as to ensure better resource utilization in such schemes in several ways, most notably by introducing opportunistic non-orthogonality in transmissions, incorporating channel state based scheduling decisions, and employing adaptive modulation and coding.

We investigate hybrid macro/microdiversity employed in the reverse-link of microcellular wireless networks where low power, small and cost-effective radio access ports are linked to a central unit. We propose a new macro-selection strategy to improve the conventional macrodiversity selection technique. Furthermore, the issue of macrodiversity maximal ratio combining in non-CDMA microcellular networks is investigated as a natural extension of the soft handover of the already established CDMA cellular systems. The proposed schemes have performance advantage over the conventional macrodiversity method at a modest extra processing. Although the presented analysis is for a non-CDMA system, it can be extended to a CDMA system.

Call admission control (CAC) is essential to guarantee the signal quality in CDMA systems. Signal-to-interference-and-noise ratio (SINR) is used as the criterion for user admission by comparing the SINR with a predefined threshold value (SINR/sub th/). Lowering this threshold level is desirable to reduce the blocking rate. However, a lower bound of SINR/sub th/ is vital to keep the outage probability (P/sub out/) below a maximum value. In this paper, we derive this lower bound of SINRth (SINR/sub th-lb/) for multi-class CDMA systems with imperfect power control. SINR/sub th-lb/ of class i (SINR/sub th-lb/(i), i=1, 2,...., L) is determined by finding the relationship between P/sub out/(i) (i=1, 2,...., L) and SINR/sub th/(i) (i=1, 2,...., L) where L is the number of classes. Then, SINR/sub th-lb/(i) is determined as the lowest value of SINR/sub th/(i) that keeps the outage probability of all classes below the corresponding maximum values.

This paper considers the aggregate signal to noise ratio of amplified relaying channels, where the term aggregate refers to the inclusion of propagated noise terms generated as relaying terminals amplify both the information and noise portions of received signals. This consideration is motivated by recent findings for wireless relaying networks indicating that the performance of amplified relaying can approach and in some cases exceed the performance of decoded relaying. Expressions for the aggregate signal to noise ratio are developed for general amplified relaying channels with a given set of source, destination, and relaying terminals, link connectivity link attenuation, transmit power, and receiver noise. These expressions provide a method for analyzing the impact of varying the link connectivity or power allocation for a given set of terminals, and support the comparison of amplified relaying channels with decoded relaying channels.

The concept of relaying is a promising solution for the challenging throughput and high data rate coverage requirements of future wireless cellular networks. In this paper, we demonstrate that the area that a single BS (base station) can provide high data rate coverage can significantly be increased by the employment of digital fixed relays without any penalty in capacity. This network architecture, which allows two-hop links, is expected to facilitate cost-efficient high data rate coverage in beyond-3G cellular wireless networks. In particular, we considered the downlink of a non-CDMA network where 6 digital fixed relays are placed around each BS in a hexagonal layout. The improved two-hop links can be exploited to yield higher throughput through the use of adaptive modulation and coding.

The performance of hybrid microdiversity, in the form of generalized selection combining (GSC), and macrodiversity is presented for lognormal shadowed Rayleigh fading channels. The GSC-augmented macrodiversity consists of K ports in a cell, each port carrying N microscopic diversity antennas. The macroscopic diversity involves selecting the port with the highest long-term local mean SNR among the K ports, and the GSC uses n strongest signals of the N branch received signals from that port for processing. We derive analytical expressions for error probability and outage for systems employing this hybrid scheme. The expressions are valid for any configurations of K,N, n. In microcell systems substantial correlation could exist among the ports in contrast to macrocell; results are also shown for correlated lognormal shadowed Rayleigh channels. Extensive simulations are carried out to validate the analytical expressions derived.

This work analyzes the impact of having more than one frequency channel on outage probability and node throughput in TDMA-based broadband fixed cellular multihop networks. The scenario considered is a low node density, start-up network with inadequate single-hop coverage. In this scenario the user terminals themselves act as relays, therefore, there are no additional relays deployed by the service provider: besides, no separate relaying channels are used (this scenario ensures a fair comparison between the multihop cellular network considered here and the traditional single-hop cellular network). In a situation where there is outage with a single-hop due to poor coverage, a suitable multihop route with minimum number of hops is sought. It is observed that as the number of available channels increases the coverage is enhanced remarkably; in addition, the average node throughput is increased as well although no explicit attempt for throughput maximization is made during the search for the multihop routes.

This paper presents theoretical characterizations and analysis for the physical layer of multihop wireless communications channels. Four channel models are considered and developed: the decoded relaying multihop channel; the amplified relaying multihop channel; the decoded relaying multihop diversity channel; and the amplified relaying multihop diversity channel. Two classifications are discussed: decoded relaying versus amplified relaying, and multihop channels versus multihop diversity channels. The channel models are compared, through analysis and simulations, with the "singlehop" (direct transmission) reference channel on the basis of signal-to-noise ratio, probability of outage, probability of error, and optimal power allocation. Each of the four channel models is shown to outperform the singlehop reference channel under the condition that the set of intermediate relaying terminals is selected intelligently. Multihop diversity channels are shown to outperform multihop channels. Amplified relaying is shown to outperform decoded relaying despite noise propagation. This is attributed to the fact that amplified relaying does not suffer from the error propagation which limits the performance of decoded relaying channels to that of their weakest link.

Multihop relaying in TDMA networks can provide significant gains in network throughput and reduce outage probability. Diversity schemes such as multihop maximal ratio combining can be used in networks to increase throughput without consuming additional radio resources. This paper presents novel diversity-and AMC (adaptive modulation and coding)-aware routing algorithms that result in significant throughput gains and outage reductions without requiring additional resources. An important observation is the need for mixed analog and digital relaying to enhance performance when using multihop maximal ratio combining.

This paper proposes a joint adaptive rate selection and power control algorithm for broadband TDMA/TDM wireless networks. The proposed algorithm is a modified, enhanced, and more robust version of the selective power control with active link protection (SPC-ALP) algorithm proposed for adaptive transmission rate and power control in CDMA networks [R.Jantti and S. Kim, 2000]. Unlike SPC-ALP, the proposed algorithm uses adaptive coding and modulation (instead of variable spreading gain) for transmission rate control. In addition, the proposed algorithm is different in two aspects; the first one is the inclusion of the cochannel interferer assistance mode, while the second one is the use of the signal quality as a criterion for user removal. Results show that the proposed algorithm outperforms SPC-ALP in terms of the net throughput and signal quality measures such as the outage probability and frame error rate. Channel reallocation is also studied and found to be very effective in enhancing the system performance particularly at low to medium loading.

It is well known that link adaptation techniques, when designed to track the channel variations, yield a higher network throughput. In this work, we investigate the throughput returns due to the employment of various combinations of adaptive modulation, adaptive coding, and adaptive power control in a fixed cellular broadband wireless access system incorporating the effects of shadowing, multipath fading, and multiple access interference. The system considered is a multipoint multichannel distribution system (MMDS) with carrier frequency 2.5 GHz. It is observed that among all the possible combinations, the combination of adaptive modulation and adaptive coding (without power control) is the most efficient type since the further employment of adaptive power control only adds a relatively small improvement in the throughput. The frequency of occurrence of different constellation sizes and code rates with the percentages of the successful and failing links, are reported as well in this study.

This paper provides a theoretical characterization of the multihop wireless communications channel without diversity, wherein intermediate terminals relay information without employing spatial diversity techniques. Two channel models are proposed and developed: one where each intermediate terminal digitally decodes and re-encodes the received signal from the immediately preceding terminal and the other where each intermediate terminal simply amplifies the received signal from the immediately preceding terminal. These models are compared, through analysis and simulations, with the single-hop reference channel on the basis of probability of outage and probability of error. Both models achieve significant gains over the single-hop reference channel, with the amplified relaying model matching the performance of the decoded relaying model despite noise propagation.

Digital relaying is a technique that uses certain mobile terminals that have good communication links with the base station to act as relay nodes for those that do not have. With this technique, the signal quality at the destination is expected to improve since the signal only goes through favorable links and, at each intermediate node, the signal is decoded and re-encoded (so that no noise is propagated) before forwarding. One of the major costs of relaying is that additional channels are needed. We investigated the effect of using different schemes for selecting channels - among those already used in the adjacent cells - for relaying on performance improvement. Our simulation results show that not only can relaying improve the performance; but also, with power control this improvement is quite insensitive to the channel selection scheme. Furthermore, with small cell radii where there is a potential for high interference level, only a low relay node transmit power is sufficient to improve the system performance significantly.

In a multiantenna system, there is a potential antenna gain against interference, in addition to the diversity gain achieved against fading. It is well known that in order to attain most of the diversity gain (against fading), the antenna elements should be placed apart with a distance (many times) greater than the wavelength=[speed of light]/[frequency] of the carrier. The results presented in this paper indicate that in order to attain most of the antenna gain (against interference) in the reverse-link of finite-bandwidth interference-limited CDMA systems, the interantenna distance should be (many times) greater than a new parameter which is defined as the chiplength=[speed of light]/[chip rate] of the spreading code.

This paper provides a theoretical characterization of the multihop wireless communications channel with diversity, wherein intermediate terminals relay information employing spatial diversity techniques. Two channel models are proposed and developed: one where each intermediate terminal combines, digitally decodes, and re-encodes the received signals from all preceding terminals and the other where each intermediate terminal simply combines and amplifies the received signals from all preceding terminals. These models are compared, through analysis and simulations, with the single-hop reference channel on the basis of probability of outage and probability of error. Both models achieve significant gains over the single-hop reference channel, with the amplified relaying model outperforming the decoded relaying model despite noise propagation.

It is reported by Hanly (see IEEE Trans. Commun., vol.44, no.2, p.247-56, 1996) that in the reverse link of a CDMA macrodiversity system a remarkable L-fold capacity (throughput) increase can be attained by using L antenna elements (AEs) provided that the spread spectrum bandwidth approaches infinity. In a finite-bandwidth system, however, the increase in capacity as a result of the utilization of multiple AEs will be less than linear due to the presence of the correlated interference effects. In this paper, a spatial correlated interference analysis is presented in order to investigate the effects of the system parameters on the severity of the correlated interference. The results presented indicate that the parameter which we defined as the chiplength ([speed of light]/[chip rate]) plays a role in macrodiversity systems (in regards to the correlation effects) similar to the role of the carrier wavelength in microdiversity systems. It is observed that in systems where the size of the service region is large enough to enable inter-AE distances many times larger than the chiplength, significant (close to linear) capacity gains can be achieved by employing macrodiversity with many AEs. If, on the other hand, the service region size is not large enough with respect to the chiplength, the returns due to macrodiversity will not be as high.

It is well known that transmit power control is an essential component of any CDMA system. In conventional cellular systems, the interface between the wired network and wireless medium is the base station antenna (or closely located antennas if there is microdiversity) placed at the geographic center of the cell area. With such a "centralized" antenna, however, it is often impossible to maintain perfect power control (even if the power levels are adjusted in a precise and instantaneous manner) due to the fact that the required transmit power levels would occasionally (or often) be beyond practical limits; this is especially true for environments that yield high path losses. We investigate the peak and median transmit power requirements in a CDMA system that employs power control with a distributed antenna architecture. Computer simulations show that by using such an antenna architecture with only 4 antenna elements, the median and peak transmit power levels can be reduced by 13 dB and 20 dB, respectively. This may yield a substantial increase in system capacity, as well as a prolonged subscriber battery usage that may be critical in high data rate applications.

The Local Multipoint Communication Services (LMCS) refer to millimeter-wave point-to-multipoint access systems that provide broadband services to both residential and commercial subscribers. Coverage is the most important problem to be resolved for the successful deployment of the LMCS systems. This paper reports the performance study of LMCS in relation to transmit power control. An extensive simulation model has been used to study the effects of a number of factors on the system outage performance. These factors include the propagation model, power control command rate, step size and dynamic range.

CDMA sectorized distributed antenna system constitutes the logical extent of the soft handoff scheme where all the users communicate with all the antennas in the service region. It has been reported that, in the reverse link, such systems, in comparison to conventional single-antenna systems, have the potential to achieve a capacity increase which is equal to the number of antenna elements used. In order to achieve this gain, the interference picked up by different antenna elements must be uncorrelated. We present a spatial correlated interference analysis for the simplest non-trivial system which has 2 antenna elements and 2 users. The effects of system parameters and user and antenna element locations on the correlated interference are investigated. This work lays down a foundational framework that may yield a comprehensive understanding of the performance of CDMA multi-antenna systems.

In this study the relationship between the number of antenna elements (AEs) in a CDMA distributed antenna (DA) system and the yielding reverse link SIR is investigated by taking the power control dynamic range into account. In environments hostile to propagation, perfect power control may not be realized with a central antenna (CA), because this would require an impractically high dynamic range. This situation may yield a significant decrease in capacity. In such environments, the DA system is an ideal solution, since as the number of antenna elements increases, the dynamic range of the power control decreases. It is demonstrated that by using a DA system with as small as 4 AEs, a capacity increase of almost 30% is achievable, compared to the CA type. However, in a single-cell system once there are a sufficient number of antenna elements to implement perfect power control within a reasonable dynamic range, there is no need for additional antenna elements. Also, in a multi-cell system with CAs, the occasional transmissions at very high power levels in order to maintain perfect power control cause significant intercell interference. Since with the DA such situations are almost eliminated, the intercell interference is kept at a minimal level. Therefore, the DA is an ideal antenna type for both singleand multi-cell systems employing CDMA modulation.

The CDMA sectorized distributed antenna (SDA) is a novel antenna architecture which yields an increase in the reverse link capacity, in the order of the number of antenna elements used. In an SDA system, a power control algorithm that balances the SIR should be considered, since the conventional power-balanced power control algorithm results in considerable disparities among the SIR levels of different users. However, SIR-balancing for the SDA system is more complicated than that for the conventional central antenna systems due to macrodiversity. We use a power control algorithm which we refer to as SIR-balanced macro power control (SBMPC). SBMPC can be viewed as a special case of the power control algorithm introduced by Hanly (see IEEE Trans. Commun., vol.44, no.2, p.247-56, 1996) in the context of CDMA macrodiversity radio networks. In SBMPC, the set of equations to be solved are nonlinear (due to diversity) which makes the solutions for SIR-balancing algorithms, given in the literature, inapplicable. Therefore, we propose an iterative solution to the SBMPC algorithm which always converges. Because of the non-smooth convergence characteristics of the iterations, finding a suitable termination criterion for the iterations is a nontrivial problem. We suggest a multi-stage criterion which yields very low disparities among the SIR levels of different users for reasonably low number of iterations. Although the SBMPC algorithm and its iterative solution addressed in this paper are presented in the context of SDA systems, they may have wide applications. One such application is the power control problem in cellular systems employing macro diversity.

The Steiner minimal tree (SMT) architecture is proposed for the wired-network infrastructure of wireless access networks. It is demonstrated that the wireless access networks having star or bus logical topologies can be realized within the proposed optimal SMT conduit structure. The SMT architecture results in a significant reduction in conduit length compared to the conventional star type, besides it provides more flexibility and robustness. For the systems which have logical bus topologies with centralized complexity, such as the distributed antenna (DA) systems, the SMT architecture is optimal in both cable and conduit lengths.

Microcellular and distributed antenna systems are two promising candidates for implementing personal communication systems. Antenna interconnection strategies for these systems are studied in order to determine cost-efficient as well as robust and flexible architectures in hexagonal layouts. To this end, some results from minimal networks theory are used, in particular, those dealing with the problem of Steiner trees. The significant reduction in conduit and cable lengths that the Steiner minimal tree (SMT) architecture provides over the star type, especially in large networks, is demonstrated. It is further shown that the SMT architecture also provides more flexibility and robustness compared to the star type. The suboptimal, but easy-to-construct, minimal spanning tree (MST) architecture is given as well, and it is compared to the SMT and star types.